{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Simple Convolutional Autoencoder\n",
    "# Code by GunhoChoi\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.utils as utils\n",
    "from torch.autograd import Variable\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded\n",
      "Files already downloaded\n"
     ]
    }
   ],
   "source": [
    "# Set Hyperparameters\n",
    "\n",
    "epoch = 10\n",
    "batch_size =100\n",
    "learning_rate = 0.0005\n",
    "\n",
    "# Download Data\n",
    "\n",
    "mnist_train = dset.MNIST(\"./\", train=True, transform=transforms.ToTensor(), target_transform=None, download=True)\n",
    "mnist_test  = dset.MNIST(\"./\", train=False, transform=transforms.ToTensor(), target_transform=None, download=True)\n",
    "\n",
    "# Set Data Loader(input pipeline)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=mnist_train,batch_size=batch_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Encoder \n",
    "# torch.nn.Conv2d(in_channels, out_channels, kernel_size,\n",
    "#                 stride=1, padding=0, dilation=1,\n",
    "#                 groups=1, bias=True)\n",
    "# batch x 1 x 28 x 28 -> batch x 512\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Encoder,self).__init__()\n",
    "        self.layer1 = nn.Sequential(\n",
    "                        nn.Conv2d(1,32,3,padding=1),   # batch x 16 x 28 x 28\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(32),\n",
    "                        nn.Conv2d(32,32,3,padding=1),   # batch x 16 x 28 x 28\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(32),\n",
    "                        nn.Conv2d(32,64,3,padding=1),  # batch x 32 x 28 x 28\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(64),\n",
    "                        nn.Conv2d(64,64,3,padding=1),  # batch x 32 x 28 x 28\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(64),\n",
    "                        nn.MaxPool2d(2,2)   # batch x 64 x 14 x 14\n",
    "        )\n",
    "        self.layer2 = nn.Sequential(\n",
    "                        nn.Conv2d(64,128,3,padding=1),  # batch x 64 x 14 x 14\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(128),\n",
    "                        nn.Conv2d(128,128,3,padding=1),  # batch x 64 x 14 x 14\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(128),\n",
    "                        nn.MaxPool2d(2,2),\n",
    "                        nn.Conv2d(128,256,3,padding=1),  # batch x 64 x 7 x 7\n",
    "                        nn.ReLU()\n",
    "        )\n",
    "        \n",
    "                \n",
    "    def forward(self,x):\n",
    "        out = self.layer1(x)\n",
    "        out = self.layer2(out)\n",
    "        out = out.view(batch_size, -1)\n",
    "        return out\n",
    "    \n",
    "encoder = Encoder().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Decoder \n",
    "# torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size,\n",
    "#                          stride=1, padding=0, output_padding=0,\n",
    "#                          groups=1, bias=True)\n",
    "# output_height = (height-1)*stride + kernel_size - 2*padding + output_padding\n",
    "# batch x 512 -> batch x 1 x 28 x 28\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Decoder,self).__init__()\n",
    "        self.layer1 = nn.Sequential(\n",
    "                        nn.ConvTranspose2d(256,128,3,2,1,1),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(128),\n",
    "                        nn.ConvTranspose2d(128,128,3,1,1),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(128),\n",
    "                        nn.ConvTranspose2d(128,64,3,1,1),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(64),\n",
    "                        nn.ConvTranspose2d(64,64,3,1,1),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(64)\n",
    "        )\n",
    "        self.layer2 = nn.Sequential(\n",
    "                        nn.ConvTranspose2d(64,32,3,1,1),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(32),\n",
    "                        nn.ConvTranspose2d(32,32,3,1,1),\n",
    "                        nn.ReLU(),\n",
    "                        nn.BatchNorm2d(32),\n",
    "                        nn.ConvTranspose2d(32,1,3,2,1,1),\n",
    "                        nn.ReLU()\n",
    "        )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        out = x.view(batch_size,256,7,7)\n",
    "        out = self.layer1(out)\n",
    "        out = self.layer2(out)\n",
    "        return out\n",
    "\n",
    "decoder = Decoder().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "# Check output of autoencoder\n",
    "\n",
    "for image,label in train_loader:\n",
    "    image = Variable(image).cuda()\n",
    "    \n",
    "    output = encoder(image)\n",
    "    output = decoder(output)\n",
    "    print(output.size())\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Noise \n",
    "\n",
    "noise = torch.rand(batch_size,1,28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# loss func and optimizer\n",
    "# we compute reconstruction after decoder so use Mean Squared Error\n",
    "# In order to use multi parameters with one optimizer,\n",
    "# concat parameters after changing into list\n",
    "\n",
    "parameters = list(encoder.parameters())+ list(decoder.parameters())\n",
    "loss_func = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(parameters, lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------model restored--------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type Encoder. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/usr/local/lib/python3.5/dist-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type Decoder. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.2578\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.1412\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.2249\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.2873\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.0672\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.1897\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.0622\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.1737\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.1016\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      "  3.2842\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# train encoder and decoder\n",
    "# save and load model\n",
    "\n",
    "try:\n",
    "\tencoder, decoder = torch.load('./model/deno_autoencoder.pkl')\n",
    "\tprint(\"\\n--------model restored--------\\n\")\n",
    "except:\n",
    "    print(\"\\n--------model not restored--------\\n\")\n",
    "    pass\n",
    "\n",
    "for i in range(epoch):\n",
    "    for image,label in train_loader:\n",
    "        image_n = torch.mul(image+0.25, 0.1 * noise)\n",
    "        image = Variable(image).cuda()\n",
    "        image_n = Variable(image_n).cuda()\n",
    "        #label = Variable(label.float()).cuda()\n",
    "        optimizer.zero_grad()\n",
    "        output = encoder(image_n)\n",
    "        output = decoder(output)\n",
    "        loss = loss_func(output,image)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "                \n",
    "    torch.save([encoder,decoder],'./model/deno_autoencoder.pkl')\n",
    "    print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ZwuwL9Y/x7I5gIfaDb9mc44QjV4QJjm9bxLUfMpeEj4Vld4QfYLTojcya7XI7sEhzJbV5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\nuQ5w1GrItlndiFZDziiqhX83Et+bZ94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gvQA+A+B1AN4F4AO11o/c3G4LIcRqMK8l/E0Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ff522ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cMOeNcUuZME444evM6inBSQUSVV1ZVOfmNsV2ZgDm8CgAKztV4Jb3d45+BIir\noQYFss7BdQCc5esAvevrXXx3juVgQFgxYcX+XOeOGPeymA6HR467ayvwxb2bbXeP8hWrQmN1+arj\najw40+BhnEp/I8anAL86Vs6Bw2qPTJgn5hQTziGI9Xq9AW+ejKsARoGCY1kdADtAyLjKpnAlylIA\n5nzzmJ8q03YU6KonOJUf6tUt41QgzMdZLqyXGgbCa7Furi06MlbZ/dfKhKdp+tmI+FmK/j/mef77\nqvscE1bjU87bjjZeJ0u8mLu/Al9VD/4GK28dA1AdSIEuhyugwLKqhfkubSwb5qHyq8DWxbky8Lml\nTDhfzshhCzUcUdWX6+eYWWeDFVGodM57HA/GslQAzHMmXCa+Rg2hTdP2UJvSiwNf3HO+CjzRSSAA\n89JVHIYccfijjp6v5XtG5GMx4b8RET8ZEVm7++4GxYRRcbyqAcPc0F24khGQHrkfgTiPcZ/1Uxt/\nOAY9PRsv5ove3umKy1IZtiq3c4Acx2XlMlbDIK4NlD5dGSNiZ3UEjgkjAOe3I/JljWTOzISdHrnM\nzmFWdXP1xHBXX16qiXEZdmDoHDGWWfUhfopDwOOhENQN2weCL+p7dJ9h5UQqXTpRZKkLoxzCmPD9\nPM+/seQGBcLZSPkKpVJMNn5ev2SP+Sw5ruIR2FTjKwfCAKy+2JVppJGmgTsmUzkBBcJKVOfHcitA\nRhDGBfGq042Ot/L5EUYyz/MOC+YvqDETXq/Xmxc2EoiXrI5gMGNgq+qZbZVtwg6V9a+cEIaZFXbg\ni9dy2atw96SG51hHbAN87HTnwjwMw3bOT9VdeCnxQDmE1RF/9zRNvxYRtxHx30fEvz7P89+ublDD\nESkONBQTTlHhKq46p8CK2YoqL1/HdWEWwQCM62z5sVaNefHYJedX7Z0oo0MAdmF0qkp3eV453k5G\n2fCSdcJHR0exWq02bLkDYNf5uI6do+M6j7I1ByA8JjoCwOraFOfo0c7QZlV7KABWaeaWS+AqPanj\nLK8DS3R0GF/ptCIahwzC/0NE/MGI+D8j4kcj4uci4r+epunvn+f5jbupWqKGSuQ4ZMIR46zWeXaM\nwzzZmBCAmXVyOVHUOk7Hht2XuNiYsFy4VvJ99hzHQFvtHx8f4+TkZEuXOCSBnc11uE5Gyu8A2DHh\n29vbzTk3HIH6UOIAzomyT8UoOW8HHikIqF0ZqkkzLJdirmn77vEb+24HvmkT3dPREnupQLnTpSIX\nvPESTJWrFA4XAAAgAElEQVROJx8chOd5/iU4/BvTNP21iPi/I+Ifj4g/4+5TRtcZ5Ff57SjOgSAD\n5RKAxetG4vJ+V2Ysu2pYXh2BjkE1sNMTOwkuI7MEVUZX7mppnVpRoTpfdjq8pto7val4nFhT4+kO\nWF09qw6p2oPL7a7hY2xv1M08zzu6Gu0jfNyxUqybYrI4LFal5Rity3fUcTh9uj6V9cZ2rPTjALpr\n933koy9Rm+f5i2ma/mZE/Fh13Xe+850dJvzZZ5/FixcvNsedweQ1Stirq/Q4TqXXHVfXcdrOiJkp\nq3KhVIbBIIhhBF8O4zXYEXGih9PHMDOcaks27M7jOeWk3Jb2NM/z5gWM1WoVNzc3myEHbOcvv/wy\nvvjii3j16lW8fv06rq+v4+bmJlarVazX682H3dlBOv2zQ3f2odo0bQPre3JyspVvlxaHnUPDcOU0\n8hpHGkYYopNOHyP3cvkZlPMYJwzxukxD1RPz4XTxI/+op1H56CA8TdPLiPg9EfHnq+u+9a1vxeXl\n5VYcPo5XlepYhwNEDDswHkkvxRlxBfAdm8BHvqoTjXpnZaQuzMDsZtxVuAJVteFPN9WPOHNLo+c/\nX6i4BPe85/7+fgPCmTbq8PXr1xsAfvPmTVxfX8ft7e0GhO/u7nb+Ijyid9VuXVzq2wFb5WyruMqJ\now2MinJ+78MYl/a50TSxHNjnlP27cru+g9/TSHl8fIzb29uh8n2MdcL/bkT8Z/F2COLviIh/M94u\nUfuPq/tGDQvyaR978rrqXpeWSrMqzxIAZmBl5jvC0FW5FBPrwnnMxqiAeAR8M+wmdBwTxvFbtz89\nPY3Hx8edP19kOF81znCWAZnwer2Om5ubrfLmeGaCrwLhzEOt564Ah8FWOVNsD+zQmN6S8KhjXtLf\nFLAyE+b5gaXOaYQAdWnhMdou2rPqY0oXnROrhky+7iVqvysi/kJE/EhE/EZE/LcR8Q/N8/y97sal\n3rwDYLyW4yrQHUkXy+UMzXW8CvwVIHM9nIPgRyflGJTBO9BV6YwAcbI4NaygwBj/esE/4OTt8fFx\n68PruM+/Y2RZmQnncIQC54jYAO/19XW8efMmbm5u5HBEPp0p0FH20LU/t68S1zeq8eqqXCPhPM72\ndOWqhmhG8lgiTk/O1hUQ4/WuH2E5K2fhyrSkjh9jYu6f/IBp7cQ5AFNSxY9uqhzcoOqcKkMF9hUg\ns+dWeeLGwFkBQAW6nN4I+GJcNcaLG75erH7CmS9UnJ+fx8PDw2Y9b+5PTk62wDXLosCWGXAC8zzP\nG9DFrRuOGAHg0fZX13MY49yEKNoMrx6qGF/F5Ct764YilgCSq/+S+zge+wI6FQZZVT/Xhzr8+VpB\neF9Z8tg0ooSqAZeCsAJcBcTKG1f5jcwsJxi7uioDU/WvwADj3SNclncEfCOWT8zxTzgvLy/l/uHh\nIVarVdze3sbt7e3Oet4sTz4OMgjzcS5Lm+d5A7iZdh6r4YgRsEE9VrY1Yn8qHpc0cljZCYdV2RNU\nM8yTsBiH7a5WyiwBYGWfI9cuTZsBls/xdVwm1R/VkrpPBoRHGtGBycg9SqHK2B3QjgAw54fp8sQL\nswvVEV39HBPD610dFfAqIM4JDQZiBcip21EQxuEI/OMFbpeXl/Hw8LC1uoHXGicQ4Yw1gvI8z1vj\nx5l3DnPkhkw74xiEM70R5rfE8fPQjYvjScmHh4c4Onr3NTi2jeq4Yn95DbYxskl8IlAAvASQlLwP\n6LrzVV9XuuD7eNUO5/l1jwnvJaNe0xltBYJ8f4R+E0gpWZVpiXd3jejqwnViQK4AGMvbOQSs3+i+\nAl0+5/RbgXAORyQAv3jxYmt7+fJl3N/fy7faUFe4SiLLnuO4CVQMbPM8b/1t2W0M7gzGS2yg69Td\nkA7W8/7+fgPAaBeol4xDO+FrHAgjSHHbK/BVwzQj4KjC1XVLzzuS467rgDjtl9Ppvo2OcjAgrEQ9\nEiDIdOxQySgDwQ2NmMvnjlXDuXC3uXJV5VFG79IfBWLMowLk7KgOPComjMMRCbyfffbZZp/f+uU3\nq/BxPH9Zn2VBcGYd5Z7BO1decByDsEqLpXK+asISh1eqMALv0dHRzk9FGXAcAKtr0YaR9WJbV09y\n7smA9eNsVOnQiUrDHTOZUNe7snfthPJJM+GI3bG2UXHgp0COmTA2mmKeztO7PFWc2zAdFtfpq0dL\nlfYIEI+y4PedmMs/ISMIf/755/H555/v/HIoy4F/xDg7O4vVarU1YaTGTUeO1bnK+br2V+3NjBw7\nNq+Pxrf/+E1AXBGS5UFQxSdF92SHdWSbqUCZ81PHnZ6WAKyLZz27MNqxwg8HwJiGAmIGYRxL7+Rg\nQDiiHqtS5yO2DWMpII+wk2l69/UlxSa4XCNlrZyBYhFqfFjJqEPg44r5ujQUIKeuEFC6LYcU3Md1\ncjw4Afnu7m4LEHGtcC5XW6/Xm8m2/C17RGzWGKsNXwpSj9Rd51TtoPTWOWB8Mqj2ueWKD+z0DMCV\nc8fyMZCjnaODVaDMe9UP9iVNXbwCXUc+sByVPlSeVZsx6D5JEObJhAg/6cGgVwGfE2SzKNxQyLid\nsTFQ4uPfSCeuwBjPqzJWcUqHGFc9VrtjTJ8ZFjIkfvmiCuMYr5psy3LghBgDFq6uyJUM0zRtvbSB\n6eQxTuShblyd8bjSG8crR6ecnqoXv7CSYZw85PHtrBeWgW0s2xBXNOCX77gvLKm7A/kR4OsYMwM7\n1m/EQTpHyk9sWG6+n4kRp/dkhyN43NWB1wgjGWkMx/oqg+vKxgDMx5g354kArIyg8tYKkLm81VhY\nByIqL+eYImIHbBWQ4BDEyCckcc1rBcI4KYdgjuDEbYQAr+qubEKBqbrfga67N+ulfk6Kx9XyvKyj\nsjN2nvwENioVWCqSw8MvfD7TVDpR+nUAXIGxIm0Vs0XnxmmoCdqUJwnCvKg8wo81uXNKusbgTpXS\ngR8DMHpGB8CuLsoIULpHpU4Ua1FrSUfTwDK5NBx7U8cMwPxft8yfZ9wRhJNR4+QZd54EYAXu3GlU\nvdQjv9OTcrqO/XZMmF9eSWfDAJx9KIdYeF6jcp5V2V1YSfeUxsMjrLsufUxvBHQ5fRXONLJ8vN59\nHxBWE/lODgqElfdwTKtqsO4Y72cwjtgdL844VSYFrgqQXX7cSRCEMQ1Vl33q3S13U/VV8d2eQddt\nOByhmDDWRTmuo6OjTV739/dxfn6+w2oRaBOc8BwyR647O+MMK8KghNvchfPaiuHjSyv5SyblTHIN\ntBqXHBmrZHKhjlMHzhkru1Cby5/TcoA7wphH0mcSxHbG9/KKE7aHT4oJf4wwKhnFGYZjwrlHQ+3A\n1zGozINnnTEtlTfXrysvHndMv+pY3aZAGMFW/XKo+8OxGxPOR3d+kQLbJwG4WtrGTyGVY852Qpt1\nTrACYjyP9XIAnGuoLy4udgA4JyhZhwpQqnCmpzZl2xUQOxasdMy6wPIo2QeAsV04fwfCfB3bl+o/\nTxaERwvumC2HXZwKs3RMEe9H9tuBLxtu5sWCY3wM3tyhXfmxnHgtH3dlUddwp+LxPge+KuyYMNeh\nGxPGVSwIsgnACVDMIPlVXwRjBipsFxxjVzpmXTvwZd3iyhFctocvseRnX9UqkfV6LXWoGJ86xqV5\n+ISA9eKw0pXa1HAE6mMUdJX9VgwaRfV71b5V/6+wI+IJD0csKTiKMpJqr0CN01NMYQRk2Zs6Fqw8\nfu4rh6HSUp0f9dEBMIcVo8G6MeiqrQJanGByY8LqL8fVmLBiyfM8b4GT+3syjhVnuszwsK4Ru0u2\nVIevgJf1zGWvXuV+8eJFXF1dbRwIOhnUI4Mbpl+1H77+PE3TZpkf2gDbH+ej+lIFwKwHZ5tVeETU\ntc5ZKIzo9imfDBNWDaqOHdBW5yoAdvmxYFpqVYQC4i5PVa8Mf6h1qxX4d3XG+3jBOobdsIOb6XdM\nWIFX5p+s8eTkZAfEMg7Bid+0w7ZStsd6YgDh8fXcqnZi4OV4BOEsc07I8evcOAmHf4pOtu+YMAIu\nvmzAYMy658lctuclrBv1qMTZ9AgAO2bMOud7kAmra0cIUcqTBWHVKA4k+Lhijy5cNXTlrTk/HhPG\n8ULHiLu6cl55D+dXjUlW8QxmrMvKAaLBuje81A823TeCeUyY12qinrEMeQ3+VDTLki9f5ON5fvJS\njQnzGnUFwJg+62mJsx6xgREm/PLly60P1efX3pQj436DeagNv7HMpEKtp67qwswb4ysWzOko0FTg\nX6Wjyo33556HnDhd1AnuUZ7scETVkAhCo8zRge4oi8T8HANglsDgi2G+R4EuxzFjY/DPR+JKD2qf\nYewQHajw9cim+G2uagIuJ5uYEXdMWIEwvlyA4JVjmu439pwuslp+FM30EYTn+d0Er9NZBbYVMKdj\n4TFhfp07f9eUn99U9URRAKzeyLu/v98pp1p37PqXYryO2DjdVMA7woadVP0ey4J9lvsd2gxunM+o\nHAwIcyeL0CCoPKADXzyuWLBqlM67uk5UjQUzM+Z8uJ6qzgi8OCbJaSkjYB2MSOUYFRDjul3cRpaq\n8UsaqTMc95ymdx8qR9DCsh0fH28e1TsAxjyyTqqdUBcKWNjRYvpq3BV1x8MD6mmC1wvjh+75BRg1\nnDDSdnl/2hmOlbNTdDZW2YkjT5kW2rlzXnz/iB2rvuvKgaSHy8LlZUCu8qjkYECYO1PG5d6Fc8+N\n5rwoigLJTB8fu0Z+36JAm9Nl0Bytm0pPAbkCbQcMI3rm+jidZb2ybswQmDnwcQJspo/rXfNxOwFJ\nPRm4LVlifk8CP8yObarqlHVRr9Ij4CLjdDpH0GVQwriTk5O4vLzcDM1kPvk3kfxBaeo7/wydv2XK\nP4FgXdU6X3Y6bLP8TQ3+kL2zedcHUBfKpqr0mP12QL4k7MqF+Xb3V3GjQxIHA8LpjZU4YFJA5e51\ninIgjNcoAKmMq0ofz1X1cSDMYRTHxqo95lGFXRoVII9u/Boxrunlx2V2plVHSRDOP2Pgz0EZgLE+\nbrlknmc2W+2XbEdHR3F5eRnn5+dbIJyTi7e3txvAv7+/jy+//HLzd+gREEbHwkQD64ef7VTOiu2D\n+4zTWycKlBmIOW8EaGUPnSPgenB8tnfez/MUnEbKkxwTVkw4491x54kj+skqNlD1WDHChPkel36W\nswNh3lceXD1OsfNxwNU98rm6KfBlPS4BYQQ/NVvv1r2qcB7j/+HyzxgdE8byK5b4+Pi45RRwHNp9\n7cyVlfdHR0ebYYaTk5NNWyYTzv6Rv3h69erVhgnnj0nd//CQYDDDVyy50xXWZdSGRlhoxisnpYQB\nWPW9jnhwHJ5zoPsh5WBAmGfDU7oGdo8jI1IxVTw/AsDuUcwBfNZ1pHOq9Kt7FeBW4SrOidIddhg3\n9OAmNJAR78Mi1cZDEciGFbCoduIOne2GjgFfveb1zznG2uky2w7HsHk4AsO3t7fx+vVryYT5n3iq\nnTDv4+PjHVvvAJjB0QGl6y+VLSoQ7rBBkQMHyJw2EyJVF2bBXI6lZAbloEDYDUdE7OeFRhSjwJfP\nL2XCeG8F8COsTgGzO6/AuzN2Lo9jzFyfalsCwDi0o/THgiCtJrnwPI4JMwCrcc7KEfOyubRVBGGe\nMLu4uIjT09OtdJxeU3BiLtsjX5bg70IkA76+vt6AcPdnaK5XAjBP9vK/61Q7dWQBbdzZntIBAyXO\npSB5QYcwYpPKgainK+VcHAA7WYJXBwXCrqIMWBnXSXUNekU20gpUsEErIFZp4Dk3CamOK7anzmM+\njp1wWOmDQbmqG5dhyXAEpun0nOFpmuxQBYf5R504JjwCxNlOGYcdNicJEYRzCRl+4yE/Lj+6MbAl\n+KoPDCH45taNCat2ZAfGQ0XoUFFXHQtWdsj25uIi3mFC6p6vzbzZnjtbQqxhYMbw+7DbJzkm3IGw\nCldxI4KNjiyAPSuzOge+aGgM7pl2XudYhYpjr41LuPAa9uqdXtj4uV6Kvbh7GIxRXyNMGDt8tU8Q\nHtnu7u6GmDDWFR/XUcfpWDAcETsgfHl5GS9fvtys5b24uJD2U+1542GB3G5vb3c2nIjE4Qi2QbRR\nZJmu7RzpcCwYbQbtXdkQ2xNLpssTz9VwhNMvgzyn4xiyk2r4YlQOCoR5OEJ5SuwYzB6UjAJRxO5r\nqFWj8r2qEdD4ENx5XWslbIAOgJUxjBiRY0nqGgwz8Kr9kg0ng/hnm3g8TdPW19ZwLTIeMwjjxByu\njnBOlZdwsY7z+qOjo834bzLh/C/eZ599FldXV4t1kAB6d3cX87z9/Qv+lROuAEHmj+PeygaVY6ns\nnm2/sjtlPyO25EAY12yre5H8cNnZuaFgXTmehyyUoDNW50bloECYK8sKd4/HeC2LY4aK5anH/IjY\nMkRlDE44PzTyJY3EwIqdCeMrg1HpqbrwsImqzwggd6yXwQeZavXL+WSeI79Oym8qLBkTVszItUHE\nLhNOEP7888/jG9/4Rrx48WKH0TuWn6secBXEPL+bjEPARYaP9eO6MnAq581hZxsK+JaIA121Vekr\nsubAl+0t64P9KNNAPWBfwuGQFAZgLu+THI5QAKIMQIlqNHWte1xCgFedzoHvUgBGIF4i6nHJAXDl\ntZUwU8pHVuf0VIdBp5DGtw8Lxh915oZMNpdpdW/f3d/fb/bVmLBjwlW7RsTmjbyIGoS/+c1vbr7x\n0G2Pj48bJ4PAm0w4V0TkGPD19bX9aSnXEdvN2YOzD+eI8R5mjo4sdcCLbcE2PwrYCozRzri8jglz\nX+r0oFj2qBwMCKuXNbpG42tR2MMpcYaCaSj2h2Xj/PIcG6IDtRFRxo6PaHidA/nKKNDT4+Npp7t9\nNjUM8Pj4uAXCOLaJ4dVqFdM0yQ8CYRjZLo8J56qB3JQ9dWEG4fxWRn7p7MWLF/HZZ5/FN77xjfjs\ns8+2hlPwFWyOw+GHdDgIyAm+r1+/jtevX28xaARzjusIg5IKpN2TQjUUxnp04IsEINNBJ+/aq7M1\ndEjch7DMXB/HhvM+NySxZDXFwYBwhGeqnVTXKGPoDIrDCnhdeNR4O+mYBIIlLrzHCYXRPaftDDrT\nrj7+or550H0vIl/Q4HJh3rhCIMeGlcPhzle9sly1m2uzeZ63OmaC52q1ipubm3jz5s3WG28I+pi/\n2u7u7jZLztyryJmec2gOpBRbrbasu7IZ1U7Kbh1xYUDFY3T+uF6c2yDrn+VgMFekzDF3bmfV96u6\n7UOsUA4GhEcZ3PtWuAIkFcfK7kC4KruLyzTcdQoscTwLz6kXHbB+XLclW8T2h9QdsPIHZaqNl5u5\nurrHSa4/sx9kwvyY7tqtYzG4vAm/5Xtzc7Nh5XnNer0eYqsJ0sl2cdmZWvFQgS+DX+oM9VxtS0A6\n4t2QlmK0GM49Dnkh8PJcB5aZ27ti0yydc1lClFS/V/kuwaknBcLIYpWhuXS7/Drv7wDYefIl5emY\nGHtfNjw8p15Y4I1XZoyAL09o4IoA93lKfnVXbd3flbkDMwizXtQYIE/M8XIv1S5Vp0TdIhPOT0lm\nnbJcq9XKTsKpuGS+uSETVqseFAAqIM5yjyztw7ZQdoRx8zzv1MVNPKr5BgwjGLOuFQ7gta7+mLYj\nJMqmXB9km/uBAmFU/Gh6eZ+K79giGlmm07HEDoCrcqIwg+FrGHwZMHGBOxoyL3znPCsA5jFQ/h17\nfu82w/ziBG/uTw6O3SZIKZ0oxpydv3pRgzuPehJSexyOyLHsZMIM0Dc3NxagFGDxCgi39heZsHoM\nV3XDtnNbsni2G7dPHbhx7gynTrJPKUDGPetd2QWTMgeIqn1V/69kKeh+kiCsGqpStupcEcvHx/Ke\nkQ1lSSPwPZVR5DVqPAw7By5ET6OPCPkGknM0qnNP07tfCiX7zTfEcKuYFMfzd4QVG06QUmVlQOMV\nB/wFNQYx1HnlmPNYDUfkGHDGJ0M+Pz/fYehuY+bOK0UUCHfjwSjdUBIOpbBzdMMWqWNex5z7HBeP\nCMuGEaARqNVTCPYBDFf1V+lU/X1URnQ+IgcFwszQuEFGAFilW3U0BxQ4PlmNvbmGYOCvpAJf9vas\nH6xP6osZJwsCdKY7wvgzL2bC+aZY/oDy6upqh82wYPwoAHcg/PDwsPmrRn5vAcGXhyNUm41samLu\n9vZ2Uy6crMO/QDNwqmN2Gs6JjNojMkZuu2ooafTphV8ewT0u82KwZRDmOAW03C84zHbs7K0C3hEw\n/tAk7KBAuPJY3Fgo3JEQMDjswNaNg+G9ih1ivAPLkTIvPaccS+6zwyiDxLKMgi8yRmRT+NEa/AHl\ny5cvd8qqDDbD6jOVeJ4f2zmth4eHrf+jZTjv4TfwEPxQLyPOmUE4mXCWl1dL5A9H3RguT2CpR3v1\nmM8sugIEtIujo6MtEFZ/6sCxYd5zHL65d3p6GqvVyv5CKoclkg07EEZ7Q2BWNsv2rcKu7zMpq6QC\n2x8YEF7CfvNe1QgYNzLmFbHNhNFIsPMku+S8lWQ6Kj7vd/fh3tUbJ4YwPveKdXQAzJMl2JHxB5T5\nui7fq0Ajw8oRZvmxAzMI4/0JvggSCYpqVYJiwqgf5ZRzj184w/W9CMC4+qNz4KyraoKLwypt1B3W\ni4cj0IleXl5uxvX5W8kMyniMjD8BOPWTOsoyZzkU8CJLVnbJ9qzAr+sX2L4KgLt+5/L+gQLhymO5\nSmNDV+mq8S9sHGfkCMDOe3fiwNfd75gAAysORYwYN+4du8qOqDpxsuAE4WpJFsa5iTkGcLw+9Z77\nBGJ8VMb7FXvkMeHuKQnTjnjHhHFYgh/fK52PbKodHPhWbYsgzO3H4/mnp6fDH0nKl2v4/3YpPJSE\nLFgxVBwbRufP/QHrz6IwQoGwAuAlgOwchDqu5GBAOKIeQFfxI+khAHODc8eqOhsahnpkcmNdqnGw\nPKreKn6086o8p+ndL22yfkvAgMEKhyP4L8D5plgClHqVNsNZP56Y4/oyiCvgVY402w7roIYB2K6Y\nBSv7yLSz3XPySbUn6pnrpvajUgExp4cgjBNx2X44po8gjMsI1V9D1uu1/JFqliPbO58WEIAZkBmY\nE2DxKcw5I6V3BawjAFzpW7Ul29FSORgQ7sB2FHhVutxIjmGjoSKbUh46omaiHRNGMMY4LCfHj7Ah\nvD7TwiVCyB6XMLPsCOqRFocjkgnzJBNOLjHYLmHCeT93XjV+i+mMdJzKOTMQ87AKlpXD3I7qOMPV\nJDHHVSxY2Z5jwgnC2Yb4og1/mY7DPASB7YYAnGPjCmx5U+w2hzO4TZEYcVvy3m14HUvV1z8EAEcc\nEAirylTjXtVkhGuQFMV4FQi4MnIZqg5XsZ2qU/Ix19fpLOvbORp+bMz7kDEn0GWHR4akmCvqQo27\nKmf3+PjYvtTBY5KqY3U6dDrj8IgjYkam2gWvQ/066dgai+v42B4cz22POlWfBnVvOOKxsgsHxrli\nxTmwJYBWsVnWm9Kja1vOgzFIYc2+BDHloECYFcFMiBsQjyNq8OVGUpNBTqEVk1KNhPdxeCkz4vz2\n8b6O0akxY8ecEyxx3A8ZDepCfa8XBcFgBHQxjKynYjGdE2T9sjNRnZNn9R14IPiOAC8fK4BZ2tlV\n2g6I1XBD9TIHg7BbETHP8xYAIwg7QuO2USar9MY6QHFtnXlWRKtKdwkoHwwII5hinAJiBX4pSx5D\nHAhXUrEk1cG74w6cFfCPgPEIC04mrK5TE1jqNWMsJ0/CODacQyEdCKvNdYIRfVdAnMfMcrFuqKuu\nDRQBcO2kjkcczRKpbADboQNdPs612WpSjplwjps7G15KMEZAmONQuH3RgWA7jzx1fxIgzJ4oYns4\nQoECAqBSRgW0lVfFMlWbKhPeW9WVwxUYd4aqmCbWX7F/ZrN4jXvyUBMwaLg4FFGxxNySiY8OQ+Tw\nRaXPER07vWenw87HKzGwc7o2Uu3Acep49FzXwdV5ZwOsawe4+HEm3CcIu6VpaQ85H+B03+mS69E5\nqwqUMa+IdxPv3MZYlgqAI3Y/+sTHlRwMCDsm7EBXgR92ImV0DMh5T3Wc4pgvA9WojBii21egMgrA\nuJaWATjjebyOmTCznmS/2dmqpxW8l2fcOzCumGylmyqcxwiweA121C4PboelcfsKAgfHOyfsnkYY\njNUX8vLD+fw1PNQJD0eo8rG49uX6YJ2X7jmviN03UFV5VNnet+2eDAg7QGbFOCasHsGrPYpioQzG\nFQh3aao8qmtcHNcbw67zZdkTfHDVBAMof34SWU9ejx/aUXpRTydLhiPSRlTbo24qgHRxKh6Z0Whn\nQzDcFxy6so44fC4H20AFvI4N83Z3d7eZmGObQCacwxEV28c4Z9eOcKn0Ot2yjezTFu6ej8qEp2n6\n/RHxRyLixyPiRyPip+d5/kW65o9FxD8TEd+MiP8uIv75eZ7/VpVuNhqKA2G1IUOBckjjG30E5PJh\nOd2+AlzHrlXYxXGalSg2zBuyv8rZRMRWZ3NjwtnpXD2QTSboLwVhBH2nj1F9cdt0esU8HYBg2D0W\nu/C+zoMBKq+tnoyqCTrFhKuP8+eYsLIJtTbc1Z/r6kBPXd+dV+y2022Xx+g1lezDhF9ExC9HxJ+O\niL8oMv/XIuJfioifiYhfjYh/KyJ+aZqmv3ee57VLVDFhBQbVlvd8VQ7r/bvG4DJU5VEMHXSxkxbn\n7QC3AmBXPsxX1V/pY6QcKY4J57XY4SojZPYyMgSBS9rQ8eIjZPXkUOkL4xUYI+NiwHQA4sBGpcP6\nSL13oNzZripjNxTh2LD74lp+QY4nbNkeckzY1X3p04bSuYobSa+aZ3BOQjmRpfmmLAbheZ6/HRHf\n/iojldO/EhF/fJ7n//yra34mIr4bET8dEb9QpCsfBbtNAXGKAmBclpX5joQV+KpjzNsxYFVnBbyq\nTp0oJqbAuBpfdWH1NwwsKzLhEePN/RImjIv2cRiFy7uP7ri+eVwxSxR13AEvb86euRxOEHh537Fg\nforpr3kAACAASURBVDuOJ+IYgHPPjhnLzEDMk8FMDjrpwK1z/qi/kX3lKCrn8bGZsJVpmv6uiPid\nEfGXM26e5y+nafofI+InogHhkeEIx0S5M7KieDii6qyOhVQs2E0QVuJAV8W7tKp4tanx8cynE7Uo\nXzFhBGHsXG6VipqUUywt41iH3BGWODAGDW531k/XASuArdpElR3tbLSNsgwOgHHrJuRGgNiBMDvl\nHI7gycF5fvfGZD7dYB1UvbjdqjZ1UvU9jMPyujIcFAjHWwCe4y3zRfnuV+esjK6OGGHBFQBnJ64Y\naAXSFRg7BuJAtANhVS6uo0pbeWtmwQzCIwxDDUdwXbLD5bAPdy5VpiVL1JAJZydRTtSBMNeTO1QH\nwBl2ulPt5JifOlZj3py+EwYIBcRqXsCNCatJOQRfxYQZiHl1BIJvtl8Kz1G4+rJ+R52TEtS1639I\nIipn7MrYycGsjlDCFUSPFLHdOdgIeBwU08P7l27qvq78KszpLNVLJw6YXBlHtvPz852Z8Oxo+aiZ\neWF7VNs0TfZHnG5lBQIKAjGDctc2XbzrWEuBtYqvRDlwHgfneqp2x2uRmeZ3gG9vb7fa9f7+Ps7P\nzzd/BlF/8sg0+S/Wru1Qbwj86AAwLuvuhv0ybTWe24kiUaN9HRcBoKPkoZQlffpDg/B3ImKKiG/F\nNhv+VkT89erGL774Yqci+WER7ljZmNnpHh8fbUOywS9huMhyl6wFdiCG56o8nUFwHkpcvTg/ZO4V\nQ8KNHz8j3n3OETt5Mh4EYhc+Ojra/EOt+jW90y0CMetnH4bEjlKB8gi4Vjag2qwrD4pj/1W6qZ8E\n4fwY++3t7aY98vx6vd584D3Dd3d3cXFxsQOy7h9+CGyoJzX8oZ5+8qmAHcA0TTt/FnGrkpyM9C93\nD4YTkF+9ehU3Nzdb1y9xDh8UhOd5/pVpmr4TET8ZEf9rRMQ0TZ9HxD8YEf9hde8P/dAPxdnZ2VYc\nd0D0QNnJsgOyR2W2pRqpAr3q8cQ13hJWmUbWlSHTXQoolTfn8qsOkseuk2CnxZcz7u7u7FCCYz/4\nN2H82hoPOzk9MgCjDkb0lDrgfRe3ZFN5ZP7KKaMoRu7q5uIxD/w5KQ4fIEPGn7ZWjjGdJ/4JGkEy\n8+Y+Wn0U6OTkZKs8yLKzDrgstFs26HRUsV5sG9xj/kkA8zOgKOv1Or77XR6V1bLPOuEXEfFj8Zbx\nRkT87mmafm9EfH+e578dEf9+RPzRaZr+VrxdovbHI+L/iYj/dI+8tsC2AhYFwq4T5P0qnaXjz5XR\nO6aEg/xusjFCOx2WLg7rqeqLZUXg5XFBfrpAEM491nVkoi03xYR5WIL16oAXrxtpJ8eylx4vAV51\nXNkRAjUfj4piwqvVapNOOlP8XdHl5aUdImIQds4T65H90k36YVxEbJ6o8EkL+4cCYaVb1iXqmzEl\nRQExn0ubV/l9bCb8+yLir8TbCbg5In7+q/g/FxF/aJ7nPzFN01VE/Kl4+7LGfxMR/8hcrBGO0DOM\nfL5SIE8y8CSBA2BMh4EQjalbDsf1YABWAJYNzMCvxpycDjiO6zmypf4SgN2ifNZ5hvPrWFguN/Ou\njvO37tiR1W+IHPgpQEbnjcKdCfVVAenIOXfcibMlLBfWqUtTnc97E2zX67WMY2Zb/Zcvh5K6YaRs\no3met9q+egEkIjZv2DEAq7iu/hiv+r/ap/AxliHrxmXo2hRln3XCfzUiShoyz/PPRcTPLU17VHkq\nXE38qHRHAFixY+UAsoxodLmvABkNVaXNgOzqUNWrqisOdzAI4594c+P/nVV7BFm1xzB2fDe2yLbA\nHQCBmHXrbIf11zHbCoSrsMqrO8a67hPH55DxJhPO4wTg09PTzdAQtgOzX9T9yJhw3qOGI9S64xyW\n5En4LDsDsGKd6smjswcOV3EoKn/+CH0lB786ojrmc2plxJIxYTUTW82cqjKorXIMDjRxzEmVG+/v\nDKxjwQ6Ec3ImN/wNO3Y2/EpW7hXYuo2HI/LX9O6xNuuMAIx1SaYyyngyPec0OxDGtuc41UbqmNtV\n2VYXdnFZl4h3oIsMmJ9YcHyXnwQxPWbC2W6OCUfE1nAEMt/z8/Mth68ICf7KKs8xMFc6UOL6snL8\nynF2JG9EDgaEXWVGldt1ILy2AiVeDVGNCy8BZAbivCYBI/NAYGEmrICWARjPj4BxpuFAOP/CmzPl\nq9UqIrb/u5dMCh9n3XpTdZzjk/wY7GbZ0Qb4SQEBzj0NsH44bR46UmG2xSpO2Yk7ZiB2wF7lp8Kp\nEwTVHGvlOrKTZUBFe1Fj+Tx0wfe4Jy50+Ew0EmxznBjjMR9X/yWgyOL6FxIClq9tdcT7SAfCI3vH\nVFzaKR0gO6aM93M9GHzVMU4wcFq57xpzpHO/DxPG36FjR+bH2bu7u7i9vY3b29u4ubkpv0Obnz/M\n45wM6hgV6piPHx/fLlOs6lmFI7Z/e1XtFQDyMYad03ZtoYBY2TTmM+IYsu0iYrOiBeuf4dQ/lo3t\nF59i1OoI9QQzTZMdjkAAvri42GHAydjVk6RaHTHinJTweQZb1Ae2GcsnB8IVqDqDrIwzRXXKEfDl\nzotl5TI6JsydlMO42sA1qPPuHejuA8KXl5dbDArZCE7qXF9fx/X1tf34S376MDdcFtW9tMHtivXA\n8ri6uvorEFYArJ6ulPA5bD8GJtX2CtzdhtdXe2TA3daNAyOIqqWF6gkGV82ot/DQ3hKEEYBz6aMa\njlAgrPRQhbk9XP9UbVWdH5GDAWEllfE5g+T7nbhOqoyVgRjvd3k6JtWBME8yqbFhNpQlDc71xLK6\nMeEEYgRMXvKUC/9vbm7izZs3cXy8/Wt1BF4EWexk1eoI1i8eq3bpwJdZXkT9A1jXdqPCj8zcZgx4\nXF926EvIBwJFgiyuwcX2wH/B4f0Mvtmu7DyrsXxcvcTpKBDmvzU7EMYx4QqEkdHm/XxOXYdtxOL6\n3pNkwqNSsb+IXQ9WMWDHiBTQVunmuKTqrK5D4/0MvJwvTjRxGB+N8pxaEK8+RZiGr1ZCVN+N5eVm\nalkgAgI6NZxcyXLz9wXU2CI/nbh2QulsgQHcMV41QafydcfdExSW1QE8O+qOfDDYcDmUbvGlCFwz\nnE85+Ipz2kMOQ7mJVQbzrj8y8XFPoU4UyOKe9a1017XFiCy596BBmB/Vqset0UezTEsxJM6TBRmT\nuh8ZA+4VK8YOruqa+SHoojHN87vvJKShZtrqUc8tO0vmkZNvDMDqjbnue7NnZ2ebcUNmLxGx1ekj\nYmt1Bb8hpb5HoHTlbAbbTjlqPF89YVUA0u0rAKlYr6qPKlNXZrQZB0TOcSIgr1arnZd37u/v4+bm\nZguI1Ys2ilUy8OPbljnv4CZpGdxVHpUTZjxROnNpjUjnLFAOGoQjtgGu8mRVp1HGiGljXpymY9NY\nHsWkKgbMZcWVEAi8DMAMwgzGWQ7FeJn1ZjjHe3PoAa9HoMWZbbfYHu9FRo46QMaTj73qkRj32VkV\nCDubcaLaFNthBNhUfsquljxlVbZW9YGqzCoNpR8mBQjEzIpXq1ULwjg0ocASiQNPvOWHoOZ5lo7Z\nDXWodlZ6rMC006FLV8mTHI7oOlfX8RjUMI4312Fcuq7TKpaivl2hHnHT4NMQR5ZaJeBWy+WQCbvl\nP7jlyodkwsyG1Vtuau0vg3G2A49fZt1w9p2HIyom7JjViN10TlWBGuaFe6d/dDIjDkPZFZbbkQR0\nvgpEeXPlccQl64CrX/AvGnndw8PDZlWMA2H1BMJMGFc/pI2r+QG36mJU2CmoTdksS5cnDi92cjAg\nrKQyQA4zyLJSOd2RDpLiAFixcQfAvKEgACP4qscvZr4KhBkQefkPhnkoggF46XDE2dlZ3N3d7bQB\nd+6sI3ZC3hQYK0bIbTTalhzfgZm7n4HXLWVUdlQxto5F53U8xo7HvGqk0otqJ2bCCMARb0E4x4tH\nhyNQXwzE+MTELwa5pyFFipRUTLh6mtiHES9xDAcDws5ImLG6MBsiGmdESIN0m5OuEyEIMyCrxuW0\nFaAqEHZlVkyYl5sh6OJxB8RqOMIB8Hq9LvWYHTBFfS1LMWCciOR2cPGVcFs4FqmuxbowA2YwxvvZ\nRjltBbSuvdPGKnvHpyxMT+kB01AgjACMdc3JO7VUDW1ZMWHMA69xY8LMhpU+UX987MgT6ox1ideo\n+5R8Uky4Ahw2fjQgHJKIiB2DrEDXsSwHvLlHQ1YTcmpp0RLQ7Y6z3jwxx0xYgS+PCTsmrF47VmPD\nihG6TY0DVxNzrD/VqarOpo4dE3JAjDaCgOKcBpIAZzfTNG05J9XGvP4WARjTRXt3T1SuHCkMwgqA\nEyzxtXX+mhrXGeuSq2W4b6jhiO5bIlwvrqsS1w5KH509oXzSIMwGhSsDFJChMhwAKzB2RlqF+TGw\n2qORsMG4cHc+6+uWoiHw5oQcTtIlEOP97vOT6o04ZMMVKHEn7kCYJ+Yc+0FRbBPPqbgRIFb2iWGu\nV7YJ2kiVN4Imt7sCd7RxtnfMU9lSVXd2kJgujhXP8yzXGePwkdI5AzGz2HnenZhzK2QwTexLqr2U\n3XDd0SmotlJhluocy8GAcMVKEWyrvRpvZUED43xd442wIwZfdewmfZRxqE7oyotxyITdxFwCcIKw\nWyfcjQk7AM4vruXGZcVxwOzMiv1WTJiBmPWZYdWW2G5V+7qNWbCyUS4zkwHOl1fGoDimzcNr3VyD\n62NcLm4nrDc6Tfz4OjvL6m1HrI8DYGbY6i0+1CH3oQ6I2SbUphwahitAPj7e/iFtJQcDwkoUC2Y2\ngAbZefu8ntPG65cCsRtH7JY7sfFw3ipcxSEIIzjym2+KCfObbd2YcDcUkZ2GOx0fV8MPblNtodoK\n9cSdpnOkzuGq9NmWmAnz47hqS7QZdiAqbZ6gGnn0dSCsdJGSefFxAnACTUeQMB8sT9oEOmrs26P/\nr0NdsROu+jWXqSNbnB+GOxup5KBBOKIeb8NGSZBwys4GX8KA8f7cM8BWa4BHOjUbkNMBl9MBcveF\nKgTgy8tL+2ZcNSbcTc4xACPoMpioTlZNzGE7MFup2g6PXSfr2gkdJ9bNAbACYWZpI6DvgLhjt5mX\nIxxcBi4jExYcv1VPnZ2dKpvAuByKweGObjhCgTvWXZVP6YltCuunnDjXidvsyY4JjyptNC1ubARi\nfoRcAsopS7wdlkvdO5KWMjZO24FlF8Y1mqNrnTFfBRIKVHE5UjX2y2yqerx8X1FPInmsWLWyGwV2\nTqr7lqaF5Xf5cN2yDgpMHJkYLZtyInhuZMWQSt8RoWq5ZqWvJWSp6pvMvjF+VA4GhEcMTnmsURaB\njATjRxqPFZ17HJdLgF/Cgru6qnAVFxESRCvdjOTP9yCL5bep8vsC/JF3Dju2O8LcVJlGJduy63CK\nHWY4WSGXm/WHNsplZSaIcc7xYJoRuy/DLAESxdwyTwbLyjFX6XLY9V0XxnmeLMPj4+POftSZqSep\nSl9VH0BhbHFxTg4GhJU4742GiMxSLXXJa7ghXGfnjVkSez0E4lHBNKqGHgFfPnYf0unAv2I1eD+z\nXVyWlEB8e3srX75wH+lx64FHWaUCOXUN16MDYbdHR6TKWgF65q3IQQKPG/dEu4/YBuERp4ui7Aht\nnkHXHS/RWwV2vM3zvAO+DohRpwqUU8c4vLBkzgbbrQunPEkQrjocAnDE7ppf18CZbu55DIrBtysH\npofCLLgDZQfo3DHcI2O1V4xFsaF9Ht3wPmbD+LWtBGEednDL0CoWzGVx+unAl4/ZqSs9Kx2nKKeh\n9MZh1iGXFYGE64X2xWHndF35M07pW4Gt2ztQ7cJdnGPBDMT4VJo6RFtCnBhlwlwu9VTWYcaTBOFO\n2Ih4LMgZfAoCpTJwpWS+ZlQyLwfGDmAqdtwZNzqoig2rtF1eCowQgB0TXq1Wi1Y9IPCMgLHSJ4Jq\nBcgYZiBmHVTHrrx4PZIEVR5MB8uj6o3tizZfLYFkIFH1wPQzXQTaLqyW2zlg7nTKcQi8CohR/wi+\nqV8mYKm7TmdKb5gm5/HJgLCqiDPEDI8wpu6xocuzO6eYOI8Tq1lk7hius6q6VdvoeHCWowM5dS8z\nYf7ubDJhXsXSrSVVG5ZDAYkDXgZmF8b0Kv0rm6rKqnRYlXPEZh1rVPanAAXz4/KhvtR4sNpGx6S5\nLM7mOJ6Bl8E3jyO2/3mY+7wG90vK6sqr7JRB95MBYZSqE3VAOsqo1P0KLPE6Nm5sbARjBGKVvtrj\nNV1HyzjuHNU4uSsPh9kgeUwYvzebX9RS47xucwBcOWHVXiPtw+CrgHFUEIydHp3z43IrcW2BcR2A\nuPQwjnVTAa/6oH9nmzgk5nSmdMjAy+CLNsIAzLrm4ctRB+KYMLJwNRz1JEF4RJRRpqhGVI8Oo+lj\nGqoju2t4xQRPIHI483UAzNc5g8d99ZcLB8ROF4pJzfO8MxzBTNgNM1TDDqqtljjQimGNOLul6XbX\nVmm7ejmwzb1qF45X5yrAZ104FqyWNyr7c7aJ9a7aPTdm5A6QkZypVVApOEHXgS/rn8uIdpwbypMF\nYWfsI16djbryYF3aLv2u7GlwCnxV2bhzKMDn8rBxc9hNzDl2pMqn8qyYMI4J57/mliwdUrpx5VHx\nVdso4OW6ufu6PBwpqDqw22O5sj25nA4ouj2HlfPBYzUpx7+1QhB29shxmTfbBk+kVSCsADnCgx4O\nQTBgj27OSfATHec7KgcDwiMAWRkbeqZMD9Pl8yoNbCAHiK5z5nk1DuWAGfNV9WVBw2DG4UB4BIBV\nPipPvE9NzuHEXDVJovZLylY5Wwxz+ymHV4G6Sxt1pFiTilcdmY8TeJCRcpoIbpwnh9XxyDUMwO7b\nIXnMNsgAnJtikBzGvqzYr3PgXd+J2P7Z6ujG7YdtxnMeKq8RORgQ7oQ9vzrOiqf3U0DMDaFYQZ6v\nwJdFMeFR4FGN7YyKgVgBsHpM7EBB5YP54X1qPJhf1lhihK6eS8SxS26/zumNsFbVWRE8M21OX3Vk\nxQwRdJTNZ3sv0dUIGGecm5DjT5jyBF23KfbIG7YX6qFLt9I7638pE+Y0lDP5pEFYKUQpimc+nbjH\nUpd3lU4V79hfBfwRerzKgazaHx8fb30DgidPsGz5LQD8XKEC6sfHxzg9Pd36hQ1+wFt9aKVzWk6/\nCjTVNSPpYzrqPgXMGN8BMQJh9XRTMd9u43qP2DhePxKvjkfyWgpmaPsduDFDrvQyWrZuOELl4cIf\nUg4GhFXDOGPYp/FH0nFxKKOMGJkMx+F1mWc1zqtYSfWoiB/Twa+gpSCTVXHJbPFjPsfHx/Hq1at4\n/fp1vHnzJm5ubrZ+Z4PDD8xORnVX6XM0ncrJdY5zNNydZ0KggETdxza3pN7vI6wvLis+2Sn2N+JA\n8rpuuaL6xoj6VrH6AagDbhTVt7lfIovFtFQ6ztaXyMGAcMSYRx4F4s7rqTQ4Lo+5o0T4TlmBLu/z\nMQvLrB7vRj/E437AiWw4jSy/dJbHCMAuny+//DJev34d19fXmz/s4j/Fst65thP1onTJenSMq3py\nUO1StZHKt7p2tIO5NaiZhmPDXX0+hjjHVgExAlw6FvzMZZUXbu5LeSrs/r7NYFytuEnhPq3KGbE9\np5PHnJ7Cmvdpr4MB4VEm/KHYMKeh0sRypDhAUQBc7bmebmxXAS2Py6lxOsWcERSyAyEAowNQ4Vev\nXsWrV68kE8YhCH5Mr8B31HhdOpWTdGDsHONSqYBbOfHq0RZto9LJx2LDWG7lLBCcEoSVk+m26rOl\n1df2qtffR4csuJ87fXLd1DpgBuBqaVwnTxKEPwQQ8/0qPZRRNpfHDLx4nfPSIxMi+J1fF4dpYTjz\nRqPNcWE2Kg5P0xRv3rzZbAnC+D+xrC+DcKePUXHXqw5VAbADRbzP5YHpLHUu7LS7PJwwK/uQogAY\ngThtAYFqVEZA133mlOPUUASXH/eKVClyxY6wAnYconF4MSJPFoQZOJeuAeT03TFKp1hmX9hJHSPC\nPBX7db8SUnE59NAZHD5q8XVVOIchcnPDETj0UTGTEZ0ukaWOU5WNO5Ri1nyt6vAjtvIhRJXrQ97H\n4JsbDkWMsmAG4QpYHRgrcB516q6+lSNVaSucUgA9Kk8ahBlgPiQTxnQrcE2pQFeFVaMhE65+IcR/\nsuC/YrhHsy7ehXPD15JxpUT+2DPvQ4ZQ5fkxpGKgHUhUnQuZvEq7i+vKmPFsgyPyPkCsyjMCplhG\nBzjq3m68dxR83UqcETJR6WPULh1RS3myS9T2AWFWNDNLx475enecsoTl5PUI3O7ejgkrwMWfcfL+\n5OTErsHEV4lxwzg+j8e5NC3XBGfYTcxhp3W6Qf3sK6ojsO5ZsGwI0gpsqw7MLFqdq5x61Zm7fFmH\n+wCxKnfGIeBiOMvG+fPG4OyYcLfqwYEvrqqoiFmKszlFDBSwq7Sczn+gmHBuuIxmKRPmvFx5IsZm\n3BUAMADzOV4RwUzY/RWZt+PjY8kosJMqw65mrPOY/5bBa4SRCWcdsV3wRZlOX5XeK4Cs2GymM8qE\n+X7Hgru9sykG/FEgds7iQ4l6csAJOSyXA1zHoBXr5X/JVb++UraZ+bunCBevnvo4rNqowo0UHK7p\n5JMB4SUbp10ddyxNPcIowOiuSSDumHD+tNOFT05ONsB4dHQUd3d3ERFbzDYfC10HqI4rkE5jRRDO\nPbOo3FeOSunKyQjjwTgGh4x3dujKpDqvAmHszJ0T2RdYPyQoOxDFclbXow0wCCvbU7bIbNfFoRPl\n17q5T2eZurpy26k6V8D/A8mEPxYIV9I9vnC8uh/zrcaEmfHm35NzOzs7i4uLiw2Ar9frLaNDz5zG\ny4xWHWM8Pr67sALhBGBc4sRMY0THrDclCugrcO+YMKbV3e8AGcGhSncEQLmcH4MNV+DrrlMrYjje\nTazxdnd3J8EWh9Vwz3nhE3GKAlLuv8queWURYwmDfsqTBOEPJYppuMYYBWQnuKib0++ke2RlYOZG\nV/VSHl6NC3MnQLDF15E57MrOcQx8HRBW4gBHdSrHWDi/Cjy7+/N85zgcK8b0lwIoXq+cWBXu6oOi\nXp1fwtDdNx4i3r0ModJU4N+115J2qAAYr1fnK4KI9UMdjMonB8IRumEqluMAGMHDbXge83GMWjEl\nN4mGY2jIpPIa/njO2dlZHB0dWTbrvvXAE3GK0any83lVN1U/B35deykgUCDsxOXlAFjZBbMtxRI5\nvc4pMCgru1JxXVpcJ66PqyM7f/cq/TRN5cffecunL3TwDNIRu0snc3t4eJDhZKyqL6NuljhfHM5g\nIrTEIY3IwYBwZVxL7h9Jwymy8/wMyC7fETBgT8/gy6w1x3bned6K4288JAi7WWcHvqNvHWUZVN0x\nXKXN8ay3bv8hpXMCIzbB6TnGpa7naxQQK0BeUh/11JR7BS4OfFUcf9qSXyTC+MfHx82qGv4CW5Yd\nHRuDLR/jmHDVTmiTeFy1jQJ3DLu23EeeNAgvVcIo8CqPysw3y1yVvYrrWDAPGeQ9CKjqteajoyM7\nnqYmONwyNQXEHK4ckgNexZIdQKj2qXQ7ov+R8xUAIwBE7A5JcRmXsif3ZIHpuTapBMGuA1sFPG6P\nwMsvEnH44eFhC5j5Vfq0BwZWBuK8Bpkw617ZCuvKOSwMd0MzHwKIDwaE9xEFChwekQ6E8xrVmKMd\npgIwBcQIlvnom2H+tgNvCKqOdbt88Xyl55FNMWEVl/qt2oU7rAPkjr2PCre96pB8LZZLAaYTfhoY\nYcOjaXNZFMjwi0Kqro4R8udT1fr1DD88POx8ywT1hXbPDoyBN+Ny63Q7oisFrK7dR53qiBwMCO/D\nhF06I2m6zuSAuOr4nJ9iiOpaxUIRfHMcrWPrvC3p+N1wRJXWCABXTDjj0tCzDbgteAa6Yjb7AB/e\ni6J0i2PC+NiMe5VWVQa2KwZeVWfXNo7tKdaIdcKnKge+DoRxBY9aPpl7BOEKgHPoQukfr8PydCSM\n47jNHAB35Izv2UcWg/A0Tb8/Iv5IRPx4RPxoRPz0PM+/COf/TET803Tbt+d5/ke7tLvHg6X3OnGK\n5mOcLBjtVIp18nkFWLlH8E3mi8Y6Uveq03B9u7FhV0c2eFX3UTDOdJRx82My5qkA90M4cqVDBQZd\nR2bAq4QZLgOmAmX3BOAcDM/Yo17VF/dGNhyO4OWTFxcXW+GcYOblbEg87u7utsaQKxJSzTmgHjge\n9enaT/WVEUDeR/Zhwi8i4pcj4k9HxF801/yliPiDEZElXO2RT0Qs61RL2fSI11PXujSyodFAcHgA\njaIaEkBvryYtHOjhOKUaslBDGEuYMIcrAO7AlyfmMB3lRBQIc3m4LZxUwO2Yjtpw3TPaAndyZysI\n1gzcDMAqTrUN7zPMbFHpNsFvFIARhHE4IkH38vIyLi4uNlt+wzrLxAB8f38fp6enm2+RKLBVTDj1\nofpc5oW2x8Cq2hrJjwPjDyWLQXie529HxLe/KowryWqe599YmO4QgLosOybg0hnxeCpvFc4GRvbL\nE0+ORSrwReCpFqvjPiLKj73jPsvnJuY6fVZs2KWpjhMgnG4RLFx+rp07qe6pbAIBWIEvM9kqfwfG\nnIYD2pE9j/MzkKLNjPQHBcIKgC8vL+Pq6iouLy93QJgBmH8o0DkABuYUBnAmCdl+qCNuW7xGOdmu\nXZfIxxoT/gPTNH03In4rIv6riPij8zx//0MkPMpw3HUKPJ2xKY9Z7RVYuQbrWDBPUuA79W4JWoJx\nNUOdX1lLWTomXAFvBcAKfDkPBxJqTBg7Ha9QGHHCnVTgm53bAROzLUzPSQW6bFvOCVZxqj3RufFw\nBNanCvPqCAbiq6urzZZvXToAXq/Xm7RG50JyNRATHUV80ubQ6fO13M6q/y5p1xH5GCD8lyLiP4mI\nX4mI3xMR/3ZE/BfTNP3EXPSGUSac4iq/lE13AOw6mgq7R0AFwLkfYcLzPMuP5qjXjR8fH3e+5Drv\n9gAAIABJREFUspaTIsgYHJtwHZbDrg6ODbtrHNhzmyATRvaM9cnyKIfo6qFkiU04xlgxe1WmEdDl\nc6oN3DGzRaVb/CmAs3He4+oIHBNGEH7x4kW8ePEi7u7uSgDGb2IjE+ZhM96madoCeHbSEdtPfClq\njJz14trsQ4BvygcH4XmefwEO//dpmv63iPi/IuIPRMRfKe4bYq94vTtWzIrjnBKrDsjxfKzYkatn\ndibHKLHjZNrVO/f4XV9mCWyAHZio8o5sfD3rvQtzO7hOERFb4Iv6VADs2ovr3U1IuZUCWZ4q3tmZ\nsxE8j8dsv5X+K6bM5XDgUwFwXlv9aBaB+ejoKNbr9dZnV/GvMDxngW2tbAD148bnsc7Zp5QjY6n6\n8FI86uSjL1Gb5/lXpmn6zYj4sShA+IsvvtipXHpQxyowTilBARyyqIzDY8VcRZ128nUMkstZsYyq\n8x8fH2/SxUmUs7OzrWEJZMLdlpMgyKSzLgng8/zuDT1mrgzsqTtmrGrsFK93Dq6Lw/sZiDsQV+l3\na7BVelk3XknDDH1EqrrydepY9RNli9gX8OkrVy+wQ1F264TTTrtZ+qH2tL2KAfN5JjGq/gzOFXtm\nLECdYvybN2/i9evXW/dyWpV8dBCepul3RcSPRMSvV9d9/vnncXZ2xveWrCCvwXMVs1TKVh0T02aD\nc2zL5T+S9ggw50RadhL+eHsae0TYRfO8Pzk52fpAe9Yh08NjfNxTdeW2UAxRdXAGYq630pcCftQZ\nAzEDpALSLLMDYQdIaoIH64g24GzG2YYCWxXnwNcBCPcFHP7C75TwCxqcFtdDpctPcLjxuSWAWwGx\n639Vv61Ak50P6/XFixfx8uXLrXtWq1X82q/9mk0TZZ91wi/iLavNkvzuaZp+b0R8/6vtZ+PtmPB3\nvrru34mIvxkRv9Sl7QxSdXJ1nUtTjSFh2iOgyY+EWK487iabsKxLtwQGBfTM4qvfIfH/6HjhPHbK\nLD++Os11UobOQOQAuGJbrCeVB7Yhgy+y8mTkWF51DgFXMWEGJQRvfKJiG1G2oJ6m8tjZirIj7hvK\nTjlPBcC4Jh3nInCpVobZyWH6ignjENoIIC8FYgfAru+hcPsgGLsnNdatAvcKS1j2YcK/L94OK8xf\nbT//Vfyfi4h/ISL+gYj4mYj4ZkT8v/EWfP+NeZ7vqkQrz6XArwJkB1AIwg6AK8Dnsrg8XX1UuqqR\nFVvDe6t9gjD/CFTtcSY84h0A51Ki1BEy46o+GOYnjO4RV9VfxaduHegqZs0giW3P9zALdEwY080w\np4nDNWwXruM6HbDOGYDV0wGmiXkooMT8cugrN7Q/1/dcX1sCxAqMFfB2cc7xKd3gHofPVFtUbVPh\nUSf7rBP+qxFRfSzzH96nIAq0mFWwdJ6IjSL3zMoq1qrK2dWh88iqMatjx8xcvPqSldoQKJHxIjgz\nMGPZVJ0yPMp6l4Axnneg64CCnyKyzsheFQgzOCuAd22OYK/sg22vA12ndwXAI0CMwIXLvOZ53pqD\nwLQ5DkX1tZGxXx66WDL+69hwlkfpkgWvq+zWDV2+DwBHHPi3IxRrWZKOMopkLphuxVoVwGfaLGgI\nlewDwPwChvqCmgu7PT6i82J57JA5JuzAiAGUjbViFaq+TlcMKm5fAXfe75hSt7nJPed4K8dc2Vzl\nhFSdKn1wW+Q+Acc95WS6XK5RIEaAV7/I6phwB7zqWOnW9UeHN6h3tV6Yx8dH8cHJwYCwEgW+S8FY\nGUXEO2WOdgzVaTm8NK08rgApt1yLycMKKjz6ke1perf0DRfL4xIhZDTYsfEaNlAFXAqI8V5XdyUV\n4CgAQvB2bVfl78rEBEGBb0TY8UqeDFrScZfqg/NhxleVg+uG8XiPA2DeRn7kWQ01jAxNcB/keqJ+\n+BjbfZ7f/eIIn6LVHMO+cjAgXDUyKlQxJZeW21iZDjCrsrq9YkJYVgVGHQDzkrRu2RnO8Ff7iNha\nZ7xarSQTxuVLaZQpPIHDTAFBG+uu6svt6vRUAS13IAbNSqq0lNNQ7apsIuMRIND2EIxdvdU26ohQ\nD9wP2EGpPpjX4dANirJ/BsYlE3KKCY+GOxKkhOuObaxWhagJ2PeRgwHhCD/ZNdp5OA1nFA6Aq8ZQ\naXMezgicMXQsEDdcCI8/+MwvVeU+f3E0skXEZhji9vZ2Z8IOwSPHilE3ql7KgTggrjanLwW8Feg4\n0Ozawl3rjpVtZJgn6NAGXXlGZJT5snC/wGMGHdVXHBBzP9uHBTMTXgK+GN/1veqcaxd2mpVOlpC6\ngwFhV5GlXk3dp8aEUaHOw0fsgo0C2Ap8FbPgfQdGOOHGr4XmdnFxEVdXVxsQzg5U7ed53jDgBHDH\nhHHckNkYx+OjWge+Ks1KFx3w8r5it7hXbVSJqjsKn0u7y3ieIEbnttQ5YXk4LQZl7BN5jGCCwxS4\ndI0nvbCsXG8G5NElam5IogPijgSpNlIg7eI4nX2enis5GBAekQ6U34epVh5tyYb3VDICOtkRcMVD\nvmihPpBydXUVFxcXw2k/Pj7G7e3tZigDV02oMWEut3JejgVXILgvE+R8GYBVmarjzqZUnu46Vyd8\ntFfjsh3osgN01zAQqwkmVVe81+35ntwrBoy/sVe/t2dgHhkPVuSqKqPTDdejAmGHER8CiA8GhEc7\nI3vajFMNwmmrBegqbTSmbLRqU0bgGhXLgyCLKyBwdQNu6oULPt99gMV19s4RdMy0ayvn8PDRb2Tj\n9lZtz+my7lVbdHXhei25TrU/guS+7bKkPDwUVf0eq5rcxb6TfYT/Bp5lQhDOb5tcX1/H9fV13N7e\nbt7UTIDGfsSOpnLelRN1grbEOsTwiL6QtOB9o3IwIBxRd5CIbU/NHU0NzmMao4aMaWNDLWXCLFie\n3Ku1vmp5WbUxIJ+enu7oUoVdOdS+AgpVT8UWnOMa2ZAxchs5QHYd0jFHJY5p8jVVXGUTDmRGneFo\n/SLCgqoD2tzQPjOc9UkQvru7KwH47u4uzs7O4u7uLm5ubjYbgjBOxjmg5X2Gq+FE1jWm79oX+6rT\nBcd3zr6SgwHhriIIwBHvHuuS2TII5z2YtprJ5zy4wyfALwFhVX5XX8eCRwBYAfLJyclQ/liGpduo\ndCzYAa3alFN04IuMiieauk6OZXcdlDtxBbqVfWC+CCgjbaBm7StnO/pb+nySUmVkwadFBDkG4NPT\n01itVnF/fx+3t7ebbbVabX1Aiplwt8fJTSdYLqf7Km4EgBUT/iRBOEKvnkAlV0yIQU8NSSgWnPEd\nEPPjr+rwvHcNOzIMUW1KX053I51dsbAOlEfZMOqtAmTU3SgIJ3jjRKTreMrhV6IAmHU9Ar5MFKo2\n6Byjcy7TNNl15WqdeZar0y/XK0E5QR3/F5f/TVytVjtbMmEGYdRxB8pKvyPEqDrmPtqFUZ7kcITr\n0Nw5MIwN1IEvKkoZMANEPhrxuQ6MK4BSAIyNuYQJu/Hh09PTnXKrMOrufTo+t5Fqp9ENwVeBMc4B\nqLZQbYMz2dgxuS3QDirH4uxRXas2FmUjTv+jLFmBc/U1PY5L+x95k437zPHx8dar77h/eHiI9Xq9\nGYLIME7QsVNCHVVgjPpzulZ6V+lg3OhKow7cKzl4EGbhDsXxyuBTWciKVH4MBimqc6s4rAd3HFdf\nHvivANhNyPFwhALfzmGMMF7VuVX7dPlW4KuAl9uqagc8xg7J4OtkFICX1pmvdfmpNsDj6lwVz7+k\n735Nj6sXcrgAnWNEbO1x1QfvM/z4+Lj17WpeNcGT6iNAiUMSjAlO2AG78EgbfFIgHFF3AFauOsbO\noNJOVuQAOCJ22FieGwFizAcnMLBuqnG7cWEHvG44YoQlcqepNuW4KiCu2pDLgOu0cZwfHWEFwlXb\nYDsqG1KOxZXd2Uum4+yxs0lOe6SzK4fZxfFLPvxLejzG1Qzr9XprOCcZcTJf7k8VqD0+7n7cHY95\neIp1r0A4dTbKfpl8KLse6QMdCH8ywxEIYMqYVZj3zkM5EMH7uQN3wBaxrXyctWVjUmyhY8IMvooF\nn56eynWV2QkyLhlEB8CdwSpRzJCPnVNQrFiN0Y8AccT2BC7bDNpEVRdlIwgSXf05PvNUaY+AbsU2\nOZx7/v+b2y4vLzdvUK5Wq60nOZyIy2OcGBvpm7wOWL2Y4fTjwp1gP45YNvm5tE+kfDIgHOEb0xm6\nSxv3TlxHdZMSfOwkv9OA5cGGXjImjOCrxoNPTrb/uMHeHx8hlxphNZRTObNuY7BlFow669LBY3WP\nso2OADjpANhdwzpjdjbCdLuZet7Un5D5jcsM54ec8M3JZLG5HI1Z8QhJUZuyEQXAFSBXgg44j0fs\nWwEw3z9CSEbk4EE4YmyiB9PhsIpTaVb5jcwUK2By5VONrsDYsV8Hxqenp/LNI6XTEfAdHSuu2q3q\ncMzKu32E/yqZSle1rQNj1k0lDPJ8jkGlkqqDo210IFy9UKD+hMxvXObxarXaAHDqMgGYgZltrTpG\nXVR9c8TJ41iw02nV1t3TRAW2LszlG5WDBuHOuJmFVgDswAI9rwONEQBWY5iVo+AOVgGvGg9W3w/G\nsWiuH9df6cKdX+LlFcvh+Ao4GXgxf3SgI0wLx707Juz0wHXDeyr7xOMqbdZz5fwcADP44nGG8Qt8\nOQbMQJw/1s1lagmiCMA414ErJka2eZ4l2DnwY52zriu7rM5XwzhVWbp91c6VHAwIKxnxhgzCFfhi\n58FOhA2GabEBcMNi/txRlNGk4Ua8+4VMppMdxT025rcdEpzu7+9jtVpFRGyW/uSX0HhMWB3P89sP\nub958yaur6/j5uZGvkqK6zdHNmd8rl3cptq8AjfWM7fpKFCr9FSc0ytvrBPlUJytoK3ytVwHjkub\nyg/w4J+1cUVC9cGckf2SDevn+qFq544AqXxcXMS7jydlOhiO0L/DUm3HeNLZjZODAuHOeyDoKSV3\nAFzloQDXlU+x3GwI9KaYNjZyRGx10Gna/lRlfqAnv4x2cXGx9V2IeZ43v6fP10Zvbm42P+8cNcyH\nhwcLwrl+cykIV6LAthpn7qTLf6S8atKP0+Aw3qtA10008d4xKLYpDGf+GJ/5IJiwbvOJ6uzszP7l\nYhSI38cOHACrOAW6I2XBNuIw20aGFTDznIrbY7umVEMlLAcFwk6qzqkY68jeGUoFvio/bmAE4AqI\nsYPioyMyYZw4wU6VTDhZDj9Gjdbt8fFxC4T5ff4OhF0e+4Bxx4g5fZXHiONBkEImg2xS6YvDHRNG\noMA6cwdWbJfLzXak7EqBAuowv93AXzNzXy+rQE89VVV6V+2k+pHScwXA7OgcAHO62IbIetXw1xJM\n4fRH5WBAeAkTUmwBz3X7pQDMeXA83qcABD17Xs9vBzETzuGIZMIMIjgrzduoEc3zHK9fv95rOCLr\n5YAZy7J0c23j2Iw7p55YsA0YkNkGRoGhYsEMwhyuQLZqW9Yx2yDKNE2xXq9bIMa2dkBcgbICQdaf\nq2eWU+m6KoMDYbfPPBUAqz07uGqP8skwYWWozoPyPRhWcSiYnotXZXCdxzFhvI5ZkgPhXD6UnUXt\ncUNQ6VY5RIQcjsj3+bGDOsB34OtkBIBVOyhg4muUk1DnGHyxw/F9bl+NtasxYa4jxrt6dKTElY/P\nR2gmrIYiloJuZQ8KiFWfUA4Q9Yz5VkM+Vb6oW5xcxP0IM672TvedHAwIO4N0hugaDs9VYZeHU55i\nF6PCAIzGlcDohiOSCSc4ZofJibn8MwYy2JElSwnGCcLuG6/YQRWzdOyn0yE6Bzy3xLArJ6AAWNUh\n47gzVwBcAZUCrn0Y/8jW1RePsU0ZiPPJyg1HuOGHDoxdm3H7OrtR4Mt6ZhZe2QqHGXQ5LtsJ+221\nr/Kv5GBAWIkCPqywA+klgFk9EnG4yydFNbjrxJlOx4Tned5Mwj0+vn0Hf7VabY3nXl9fx3q93lqy\n1n0ztlodwV+3cqDrmIfSVceEK0aM+TpArq5XwIt7VzcFcBVAMTh0dcVlhQrInDgwVECpVkZ0qyMc\n4DqGPGIfWHYl6onHgW8FwlU+GMegy2Hu/6Ns+EkORzh2wEDbASOnqeKyQfEaBnZsqErhGNexEQwj\nCOMStVzPiasjzs/Ptz6ePc/z5tus19fX8fr163j9+nW8evUqbm9vd74Ri2uP+bux+JHt/M4rsib8\nsErV2Vx9VXt04KvazrENzkuVRZUTAZjb39VPtV8FxA6EeS03MrGuPlx/xcJVHDpWBOARIHbAXG1Y\nxiXi2qsC4SWAx/rmYwbjiN35H7dX9RiRgwFhJyMMVHXQ0TQ6hTJwqDDu0SBcZ0InkPcqJpwrI87P\nzzevkiZwIBN+9epVfPHFF/HFF1/E9fW1fKNOxR0dHW2A933WCWNdHfjuw4RZHMiPOD0GXw6ra6t4\nB7oqjsfmsaOnJBB0gKaOM0/+YSaCFDLhap2wAlsFvqNDE6r9RqTLC+vZpavsSQExtgkPR2A6HQvO\n+0flYEC46nwj9+J+9B5kKQzAHB4BkryePSh2ZmYX2Dl5TBiZ8O3t7eaVUQfC3/ve9+L169eb78Kq\nDc8dHx/HarXampCr1gkjaI0CMOvcMcIOgFNGQImv7YCY22oEjB0Aq32mrx571VBEFVY6SDByv4/H\n7/i6VREOiEfAdyk77toV6+zAl8uHokiaimf9Z5wiBBVZ+2RAWMkIC36ftBlUFQCzuPfK+R78RXzE\nLgDzcEQ1Joy/o08QxuGIV69exW//9m/H97///fjyyy83r6fyB7t5Ozk52UzsZSd9XybM4U7/yoE6\nMK5ASR1XwMqdpANhdf/IXrEotRQKyz2iVwWMaRfqU5EjY8IMxM5mFUCOAK1qS9S/auNK3wqE3ZNs\npqmAuBoqcoCrwHlfOWgQVpVzDb1ECc6YlQGwKE+JoFEZo3vkZoPnf3RlZ019JFgnSL98+XIDntVn\nMJP9IgvkjpTlVN+04L8ljLxr7wxcsRD3RNN1br4e13dWq0Nwq/JzgKCAEsvEZVDfdHiffbVskfcX\nFxdbjnye5w1DzieqtI08zs0NVY2w4BFR13UOSemZ49Wx67sVueL0FBC/jxw0CEeMjyNVjwbu+pHN\n3esaS4FalguBiF9tdgCcH9VOZp0dMCfvXrx4sVnFMM9znJ+fb4GLW6bGZeVy4hBJAnuVpnoq4Dqr\nx+9K2FG5+7iD4PlqeZ5ziJgGO+aK/bly4ZOO2qsPMY2Eea14Fc4nqmzHiHevvN/e3m7FrdfrrQnb\nERB2TwCuXZy+MX5Jm3OcuybDFQDzWvoKdF3e7NgrOXgQdrLk0Qele+xUIKpAF8vBTNg9ImGnVOwZ\nx/MShJMJ418McAXFixcvts6dn59b48NjVV82RATdeZ53QMyBG+aDe7cms2tP13lZt6xn7FBV2SsQ\nV7rKoQwFxkrfCMLdpKlaWuiO+UUd9fJOxuOQVj4pJOAyKCcwJ/BiGOcKRl5p536QcdhWCohHyBD3\nxY4NY1zHhpUt8/0ujypeycGD8NLO6YTBZ8nGCmXAwmuc0XBDYiMzCCsmfH9/vwPCl5eXWwB8fHwc\nFxcXdvzMxSmWkR1dTS4hiCkmURmtGwdFXVT7qm0rPSsAVkxdOUfnpKv2zvwzfQRdnBzlSVS3rluB\nsQJct6XN4C/tE3Sz7jnZyy8C8bzBhxyO6JjwEhDm9nc2UbFgFYdpjLBgF+fkoEFYNUx3DQt37pE8\ncdCfzzHw4jGXSbGAvLYajuCJlPxTbd6XwxEMwKenpxtgVutAMQ7zZH0hUOFX2RRTGAXhZF8JwAxi\nrD/UaaXPKsxOwu1HOgwDcdXWWA5mwjw5ij/cPDs7K0GYN55k47fhMI7BPJlw2sH9/f1WurjhGuMM\ndxO2I/qs+mbFqFOv1dMHS8WAR21jCRA/yeGIrvFcgyxhwxHLZ8ExHzQaBmJM2+WpDAHrjjPcakyY\nmTACcHZo/EEjd6DML/OoJuUSLPG7x50BVyCcosDY6RuFy6h07zpa1cH4sZM7lWPCrpxc52wftfpF\n/WyzA14cslDAq15NxrZH0pDOmevdsWpcOcNtN/KEUOlOpcFpc/tj31JtwHFLgFi1p4vr8ndyMCDs\npAPf7jGV2dMSAMY8XMMzuHL+LswNnB2cx4RzxQMy4fytPQJwrinGb0ngGmB85EYmrDpLli/P5SOs\nM2KOw3oloLMoAFYMM8NcTtbniIOowlU7VuXsOhs+UagliPhqeoIwTtKpYxyOQNB1v5O/u7vbcvLZ\n7m5tsPorhoqvVok44OxEtXslFRg6cGSbVXaBE9gun6V5Ozl4EE5Rnhb3nSgjGAXiDnQdkxq9VnUA\n7FgnJyebR8dpmjagiMMSOJ6M35JI1hSx/bPGPFaTiAhMql4je2bWnY5zPDbvRYeJbYXl5DJXQKuO\nMY7rqcS9HODKhPXP4SK1Djz/73ZxcSGB14UZgB0gqyEKN5GX3yZx63F53XCK0oly7qoPumP3lMS6\n7uJHHDbbzdLhiJFyODloEO6At/KU2OAqrB55KjbM5VLAjEw58+Iyqc6PQKQm5o6Pj7fSyE6Iushw\n/i0jJ3kYgNfr9aasPBzBhpnrRpnlYlmU3jvBNmAdJxhXTNjp2k28VSCsmBG3deUwVL0V06pexkEg\ndsvYFBDz2K0CZRzLXa/XGx2nrWQ8Tr7x6g8Vxv2+opjzPukusUtuG7YPnsDl9EaB+EmOCTupgHf0\ncUUB8FIgVulwXMSu8juvi/VENoydB9fodtvj4+PWUqSIbQaMQxMIxFxefERHg3wfQf1jWL0AowQB\numLCDoQrQEZgReeYe2cTeD3XdRSE8yebV1dXLQjjh5jUxJnb0mnjighcJ5zfD7m5uVn0tMd1/v/a\n+9pY3barrGeee77PvaetIK2KxCLyFQMhVQyBQg0kxpq0IRCwEA34w5CiMfyBkGiKEiVibCof10hA\nCBFI+JAoSaEVYjHlq6FGQ0FLxJaC5RZKy/nY++x7zn738se7xz7P++xnjDnXPvucd+9z10hW1lzr\nXWvOMccc45nPmnOu9To7OHFAXsW4i/Oqw+8BcW9bhiNE5jaOk4pdZZs+bnFwan4czFkPqmyN88te\n1uAhicgjexsuxoqzIQj+K/P4LXvUY5DU1QzaLi7tnFLB17WJc/BeW2cgrJ1dL+gcw1V/i3bKgszl\nq2uEMxC+cePGMaCt0gq+DozjHPAQdHl1BH+DZGdnB3fv3gVw/B+Js2O2r2s/5wsqPaKV2Zl9aLQD\nGAHel+xwhAuyUcbKUhnKgV72hptrfAVizbMqXwE4Y8OqWwByZi/XsejsuPtkYaTV9pnN1SZzQDmz\nJ9uG072tEu1ER+7jzkDBU+vJrLkac460W5ama4MZXKOTjDrwMrJox/AfN/SQjRG7j/a4teLObzM7\nO6DtAaPmMXKO24gJwmg5PR0c6YoyXTw7nR5Fzg0I9zYgnyTScxkAj7BrNXrVCFVv6xo4HCH2HDBq\nE67DarU6CuJpmja+isYL7DUYeSjC1T+zyRwAHnXQCoyDufEThjIwF0y9IRTtQOMcTyTyuHi0Xeii\nHasD4osXL24sQ+sBcOgTbdPaesmY8yWdcKvWDWevHIdE3XjlTdbJsJwWGI60kcazK3ME2DM/1yff\navmilvsodT8zIJxJBpLZGteQCogV7BwYz9VRy+IyR4CY89IZ6Hhbzum9Wq2OlisFCLu/r3cA3OuE\nsnNa7x4Aj7YL20ttp2PUzm5hF14Wp3m4dnNtxYDPzFdf4db21DSDsDJhBWAe9uHJsWw/8s2ISFdf\nx4t6cv2c3+p2msDbi7kstvT+0Ks6x08acc75TWYH1WEE+Cs5MyA8yoQZONyY5kjAK5i5vHpsWPPV\nexyYZMHq6qtMWH/j11hjH68Zu+8DMxPuvfdfbVrXDIC53toO6uCjQe/G2p39dZIt7uXx+qodlXHz\npKeCltv4t4sXL268jMGfFlUgjjJGN/fJSrcFCIcPqE9FPcOHGFxd+zm7cztm+8xPsnasytTfKvLk\ncIX3DMCh78jE7oiOI3JmQBiojVUBJ/devM/SFbA7PXr6Zo0wwoIZiFgfnaiL4AiHWa1W6eoI/eBK\n9R3ZHui6jm4EjLXOjkGonXq2cwy5ai9msBxovDpEmUzkycw7gpJBa3S1SjBhZcMMwMyGGRh6n6dk\nII50du4kTLhqrychvTiOtGO3ISNkCjj+iVoHxAy82qE/iswC4dbatwL4cgCfCeAegF8G8C3TNP02\nXXMFwFsBfDWAKwDeAeDN0zT94VzlHBjownEG4cPyN/YuPcr25uhZlc1AlLFgzssxHQ7+bM8gXI0J\nZ0zYMeOqg6rOsW7s0BWbHWHCDiCcPo4B6+9cL8f4tH6OBQeDzY6r4Qj+clrcF3rxqpZs8o1Btrc5\nH6hAeFR6rHfk/p5ou7h9Bb5Kvqp6KBAriWAf7pGwUZnLhF8L4LsB/Prhvd8B4J2ttc+apune4TVv\nA/A3AXwFgNsAvhfATx3em0rWi2WAoJNrLCM9qAPeRwFilYoF91ZHOCDWnjrblAkrELuZ8WyIxx2z\nrrzXtIIvAAuE7OwV+AKbwwwqDmC5XJ6s0zZ3PpJ1ErHXoSC3v3DhAi5dumSHIxwTVhCOJWTZv59U\nT4ZKWHTirjcx12vfXow4MB65p7J5lo572WddvlVdOB/1Py4n69RH6pLJLBCepun1fNxa+zoAfwjg\nNQDe3Vq7CeDvAfjb0zT94uE1Xw/gf7XWPn+apvfMKe+wTAtOuh2WxbqlacdwMtYzV1eVDFS0kVkP\nBeCYHee6OAdhEM7Gg7PJuQyI3dPGCBgz2DH7dcHJbeJsxR2Qs3nGgvh61sd1vGpXF/QciDoWn6WV\nCTsg5u9BRBnMhPkbINHB7u3tHatPtbkhjYoJOxu59o/jqnPM2m1UqvZge/H1VTkOgNUHYh++O6cj\nnyuPOib8cgATgI8dHr/mMM9fiAumaXp/a+1DAL4AQArCWTDNAWFgDIg5/znpnjhwib0b2LLMAAAg\nAElEQVQCcI8NKxCPCjNhfiXVDUfEMMcIo6rYRO9c2IAnPtzvjhXzb3PYRy8wnN11yEg7TD6nn4as\nvnp2Wkw4/mqI32xzNnd7HTPmjjbqx0vkepuzcQ+MT0MckQlRMsYAq3roef2N9yGuI38U8A05MQi3\ndelvA/DuaZp+6/D0qwDcn6bptlz+kcPfUumBMLM2B8aiW3rMDfQ4ZRSAWSomzNdU+9Vqla6O0Em5\n3ji7Y8mqh9NB02wHzcc9BvL1kWYmkulQsdseqGhZva36zGQPhHtjwoBnwvfu3cPOzg52dnawu7ur\nLlcK+5ZrU2Z97lrOh9vMAdFccKpisfcUyU8nwPGVMQ6ItUznq6O6acc0cp/KozDh5wF8NoAveoQ8\nSqmYsIIVMAa+KqfRk2WizqIBzk6U1TNWQYwykwBhtzrCjQmqLTMQnjscwR8dYltkM8oaMArCwMNH\nwqqzVmANBs5lZPZTPasx3+rrZvqth2xSrveyBjPh+GfteLX47t279vE5O9frvMPHwsYBZmG70MkB\nWvw+AsY9cHIdNNfDATJwfHWDq/uoDm54k/Wo/OckciIQbq19D4DXA3jtNE0fpp9eAHC5tXZT2PAr\nD39L5datW8cqHWNph2Ue7UcCujoelUc1LgMqO7eu4eQtPuLOIK0MUIXrFyDB3ypwzDfqxjq5YR79\n6HcWgHrsxkm5vUY6lKyuI4EVAKCdnktXk6XajqEDj3PzpJb7pgevC2Z7hISt3Vpf7RydjTK25/w+\nsyufrzpiJQncabIuUT4zbbUnp/U4/J6HaKoOWjFBQTmb3HWEosILp+utW7dw586djet6McsyG4QP\nAfiNAL5kmqYPyc/vBbAP4EsB/PTh9Z8B4FMA/EqV782bN3Hp0qWNc5Xj98C4B8QZg9Nz7t5MnKMx\no2itHbF2/tuZ+KiK9uQhAYR6jTvOeme9LoC6+mC3npsjFcixjRRc3Dm2J+ftOmY9PskWwiyQO1MG\n+GCvateK8TJghZ1j8lUnzbTOzLR7Pqx+MNKBRr25HbLhKgZMBcdMsg5XN/ZntXvc45gyp7XTj98y\nezmikdWJdX3uuefwspe9bOP3vb09fPCDHyxtETJ3nfDzAN4E4A0Adlprrzz86dY0TXvTNN1urf0A\ngLe21j4O4A6A7wLwS9MJV0Yclnu058bg40Rfe5w5ADdQxi6yvOOcA+JpevghFi47ADgDgbg+Xk3W\nR+IoU885AHbjmQHCzMDcSwHKYufYIuswQs8AOgfGDkg4PwfwGeBXejqdWT9ldAEKvKaWbRvAe+XK\nFbsCgsdedTgt+7YH1zsA3XVakW8GdiwZKGu+DnidbiNStbHGYkhvQow7gTjmpWRRbrQd1z3bc94j\nzHjEtpnMZcLfAGAC8C45//UAfvgw/U0AVgB+EuuXNX4OwDf2MnYVCQkjaGNUIMn3OcmCXnWq8qyC\nXJ3Z1ZMZcLVSIj7QE4HH3wrWR2EFSwdY/Lic/Y9YfI/iwoULdolcr/4u7ToYtdVIcLrxWbe5cqp9\nppvqPk3TUSemT2phV/4jz+ppQEFBlw+yMNBH2Tpmy3rrEIKrnztWwM0AWEE463g53zlb5J3FJ7eL\n8zcmaIoZ2XkuM8u/yi+zbyVz1wl339ObpulFAP/wcJuTd2pk7eVcEFWA6wzIkzw6C9zrDDhdgVIG\nwJE/z4g7Zw29VqvV0R97Rjk8NMOPwAzCFQOO8crqb3GycVKtu9trUGdpF3hVUHK93Bgs1y/0mAMo\nmnbnDg4OjkBQ20SZ8JUrV1KQUt+bpunYCzUuf36Kibwirf7DQOzaIDvOSIobKsqekrLOrZcn11mf\nRBxOaNzxMAYfV37mjjX/DIiB42PAj3VMeBvSY1J6XZUH8JAFu8eTagY404dBigMh9lweH7tH5riH\nHTTW80b+/BjMIBwTP3HeATBfGyAcKyj4Hxsca+vVW0E426ox3yogHCBxXXSL9u1NPjqgqoA7OkbX\nLtwhBghr22o7c5k8HKGApPUO8OW8og10ErjSIfO9kY2BksmBy9uVMcKwOSaruIx7QtwcSVWXKj8l\nSXGup8+InBkQ7vVwI4+3FQizBAtWYHZMJSszAyCtk+bLDuVAmJ3RAXAwQHeOwYeHORwDvn///tE+\n/qVBxy3V8dQ+XHcHwg7kNKCyIHFpri8DsPto+uXLl4/a1X1ZTM8zYGsH6rZY5pe1A4NwD3C4HPc2\no6u3gm/2xMVjzmxHTfNxr20UhBl8dRw2xLW5s4cSJI7FHlC686MAXHUyWf4VAM8B5zMDwk408IE8\n6ENGgFgB2JVXSaaP5qX58v16jq/l4OGP9+iKBmBzSVrMyDMLVgZ88eJFPHjw4IgFZ//soI+2DoSr\ntlH2qY/XkbeC3sg+A2J+KSKGAaqvkI20mzJKHiKqQDJ0unr1qv3KWWajbGLOlZGBhQI7A3ZVV/XB\nDLz4nLY/T4jFngmNAzoGYAbh3hNVlMl7tlXk4/zIDYG4IYSXFAi7ni4DXu59MyNVQJqxg6q3ZT30\nuGLCWX46juYCh5kwsywNfg58ngjKxoB5/Fff2GKbqoM6u2dtwgyTbe2eQCow0HNRZ+184j/beGtt\n898nYkmg/lMFl6N1ZwBmcJzDhOO+6BQ44DX/GBOumLCCMIMd206HI3p2VpuPpBV4HeBlAJ8BsD49\n8rCC+kHsHanhTknLj05QbahgnIF8BvAh53ZM2BlZgViXI/Gj0IgoKHDZPVBVvdz1mbPqXsfO9HE3\ngodXRERwZ0MU8XsE6/7+/hHzjZUQly5d2gAl/m4BB7IGSgXCfBxpt6Ii6q2P0i7AqmM3HhysM/5C\n/tq1awBw7B+HdU026xFgwmU6IJ6m/pgwT8zFChNua07Hm3HZt57Z53hpXNyb1Yd9aQ4Aj/hv7KNM\nBrA5qxIcAKuvcZ4ZsVFyxHpqLLshJ9U/2ijLlwH4UeVMgXAmCsBuO6lwAHIvPKqTA211Zvfo4x61\nFIADRKdpOgIanrQBjg9HXL582Q5BZH994wCY7cLjpg5se/vIk9maMs0eADu7KxAH4F29ehXXr1/H\n9evXAeDoA0b6kRxuGwUzrr8OE+h3fB1T5XFqXh3BZYUNOH/3Wjn7Cy8xDJty26gNdUhL7TvChnvn\ntONS1uo6Wj2n/h8xmE2sZX7RO8eMmTtCtpebL3J5cbs7neYA9LkA4ZCMgWZDAdW5rGGVeXO6egyv\ngDtbIuNAj4MnWLvbsr+z0cfYKIcZBa+h1Vds3eRV5McA29uqVRfuySGzubb75cuXN9ju1atXN/6p\ngl9YiXueeWb9t0/6J5YBmDxGHhOU3HkoaHK7uP9zc8v+whb8XWC3ZW3ba2830TcKYGHj7PceKLun\nJz7v8qoki6UsfvW3EUCOcxmr7T0Jn6acKRB2vc8IyIWMPD4B2HBc56wV0FZgzHr0Nn58ZfDi/CqW\nHI+wEeDxucN43GcAVTB1AO3Gj3WihMeae3sFl1iJwaswsqGlbAsQZpYZ+wDOqOv9+/c32CeAI4Dm\nJwceDuDv9jJrjicCXgGg47jxxbq9vb2NV5YPDg5K0NXPjWadrtvcP2g7hs6dWQZWCqbsx3pfBsC9\nGGW/dnHG9nWxlennnqQqXTJc6Ann2WPKc4D6zICwA9oRAHaPOVmaQVhZo+sJo1wHuhkQV49efMzg\npwCc1ZEBlT/4EwB8+fLlDcam42xZh8PgqgDMY2PurTT3+cYA4WB9AVDZpxt1FUe1uRc09OWMGH9V\ngNBXhnn/4MED3Lt370hHvjcAj8/F5Bx3hAzAUc/oEAJkq7S+qOGeSjitX8fLfFrBw4GOArLzXf5N\nfXMO6HCscDzxMISLMVeuG7vtiXs66D0lVOlR5p3JuQDh7HeWEfapj2o9AI6AzYBXx0hDD9ZHz8U+\nY32ZwzkA5iAMsNOOINtcXRnoHJvSN9IYhPVcdAoMvtkkoIIrX+8+fu4ClVlrjKkqu67SMWkZ+oWd\nAuwYwJUJc0d47969jXrG/Qq2uud/QmaS4J5meIWG+1C/G45S/67ExYT+/qjiGK6C8RwSNrcjcPGp\naadztXfXjsiZB2Hea1oNn4Ftlq7AOGPCVTp0YH2yc5GvAwQum+ujY4QMwJcuXdoAYVeeHmcA7GwR\nE0IOGB2AxqN5xn65fryyQz/9qPve0wazVJ7EYqavHUdMXMa3N1prG2xTx4m5DB6OYLswuGtnqX/c\nyed0qEyBmDcdj9YhJ0cuVKonL2aa7DsZmx4Rvlfjm+ctNA4yJqx+oE8/ThxR4vOqr+5fciA8pzeM\ndAayDnQzdgg8XMubga8DYdYpO+bHsCxvvdetnIgAj9n/AJxe56VpHo5QZsEgHSDDn2fUzzVGmgG4\nAmEAx17zrdIANkDHgVCko02iTF25wGm+Ptg02zZAONrCjcvrWDKAYyDc29wQUrZlk6gZEx71B9aB\nVz8wwD2KVEQqnkK43VR/1/nOEXfPKAPupV0de3JmQBjo9yhVzwP4tYfZuChfr2ktqwe+DoQz/ar8\nXYfj6qTDEQ6EdcscWoGW7cC/6ZpcBt+KwWZDEFy3WGKmL1vExr8dHBxsjKfGBmx+HJ2/f8FsOCsr\n1i4rAw6Gq8MR3A5uCVwAtlspkZ3jFznUb7N0NQGbxU0Fygp0MTQQv50GG2bJYrmKidCP42KEAbt7\nR0FcbfVUgvBJmXAY3T2SZluvF4z8RsHXgfBII4wEQ+hXMWH+9gOADZ1iC1YTG9uXAdidjyVe1Xca\n9LcMgLWtDg4Oji07q7bVanX0f2t7e3tHdQ5wjmGbYKWXLl06assYgrhy5QquXbt2tJ74+vXrG2xy\nf39/43/dAoQ50JkJh/3ZtqHT/fv3N8Zue3t9UhsZRsuAOnvsz9gc73VcVn3jJABcxYT6BZfl7quG\nInpyEvas+rykQLh3DZCPBTu2EIwny1/LqkDX7V29quOeaL0UgHW5F7D53VneV3WbpmnjuxExqbRa\nrY6+V6F/267LxPQv3WNsVFk4t0/ky6B448aNDYDk4/39fezs7GwAYwBw1IPZaUzSBQgHE7527Rpu\n3LiB5557Ds8+++zGUMb9+/eP/tH48uXLG51J6M9tweufuc1Wq9XGq+L6QoY7znz5JFvm07105ocO\nfEfBbE4cxG9sUyUlDNonlZMwYqfrUw/CPSbMUgGwgrA6oLJf/s0Bbg+ER1gu9+JcB033mDCDcIBp\nrHAIhssvaDBz5zryb3Ev21E/kFNtly5dsuO/ri7xuvH169fx7LPP4saNG+k+JiEVgHWJ2v37949A\nNeyYgfDNmzePrg0GHH+qyf8Np0w43jiMNckMDPF7dARuUwBerVbHAKEC2Mp/3NNdxuAyEOZ4cHGY\nAbOKi2tmrsq0XblON1fPrHzNPwNdPufqnpGzBYSRjwe7yQvNmwFIyx5Z5qQg7BxHf8uCTHt4ZcGO\ngemjfjDXGCd2wwx8TodZMiamY7S653SAsFsNoUwyXjUOsA12+txzzx1Lv/jii0eAy+PDfC7W5gaw\nBhPmMeEA/Js3b+IVr3jFUV4Bwjs7O7h27dpRh6ITc5G3MmBtHwfC3InquchH9xnwuk5ej0fAJAM6\nBcQq/kaEQVaHEBw498rWuKn2DoDnMOAR+/G1o3JmQHjEGHMqNlqG9uQnKcOBN5/PwNkFktOPf2Mm\nnI23BgNzb1kFQw6my3bJ2JZ2blVbZQ5aPTa7WX/tQN3mxvp7dajKG51D4Dzj/gsXNv8GitsrW8nh\nzrEP9MA4A8/MB+eA8TRNGx1ypPWcluf8IRONPT43B/ArYB8pO0vztc6+Lq75mlE5VyDM8qiAzOVW\nIBgTWlnvyWkGm6pH1vI1H66jsmNlWzoMcnBwcATAAcbVSxYZWPWOFcz0txhTjW1vb+9o0/MR0FEP\nXeXAL0I8ePAAt2/fxp07d3Dnzh3s7u4enY+hh9basWVlOuG2s7Oz8a2Jg4ODo78uv3v3LnZ3d7G3\nt3e0+iIYtbZdZg9e8lZ1FtzW/ISi/jkKEhm7HWHAvCnoqq+q3rF3Tz+uHpxHdo47BK2vs5FKRn6q\ne3p5ZMePImcGhMNBWaJh9Rxw3Ihze3rt9SvhIMsA2QWH5qFAXDmiA+0e8wo7MlPmNbqa5nWxvQ7G\nMchq0khBuALk1erh/6Pp4zwD8O7u7tHEHG8BljGm2trDlRCRLy8747faoswA4du3b2NnZ+cI3F98\n8cUjgM9W1oRttH2C2QbrzcBXAVh9qQIklpP4f/UbA3FVtls95ABqBIBdGQ7o1IbZtT3GGnn1cIXT\nVYdX6ZzJmQHhcGAWXloF5I8qp8mKOR2OFXrpeGlcV7F418Du2sz59H4NdAVpBuCRzQVBxs4zwO2B\nMIMuLy+LTdfoMgDH9bu7u0dL1OJ+zifYasaEeeKNf+NhD8eEA4RjyCLaRdvOMWEuW4c2KiAe9Q++\n120Kiu767Bz7f89HszKdnBSIXbnVbxkYz8WKEQb81ICwY8LAJhBzI1VGHmECKhlAKvvNWPEIC460\nSs+hVB8O8sgzAHi1Wm0sTdO0W7aWBYHuRxhwXMsA6ZgwHwd4OgYcKxX4ZQ3+VoN+BpKZMNfRvYLM\nQx6r1epomCOYMIOwY8LqDxkIZ0DMbdwDiFFgcmCoY7cjzE6fQJkRV+WOxhvn6/ztNIhVpotjvRlZ\nGu20HkXODAg7IHMMVJnfHCOE0bL73KOmA2DHgt29qqvqkunodObyuLPiAI+vdmXrhHkijz9UozpW\newWezAYZ+DomHCDohiD09eVpmtIlX/zhHv5+AzNhfruOv/vAIBxMOIYjgmXzGnO2mdokAFiHINx4\nMPtjtIe2L/uFMmj2kQyAR8HX5ZWBsuqe5af1cP5U+dyo9HDgUQAzA93TAuMzBcJuOKJiXCOGya7R\nsvk8O4Bj55lurk5VmQ5w+bzruUMfdlYenlCwdZt+30B1zQIhAxJ3nIGvMuG9vb2j8dMHDx4cfXkt\nexVa9WB2GftgwgzCkX+0aRzHa8n7+/u4e/fu0cYTczwc4dqNQTZ8jVc69PzE+YGmFbSzPBQ4MxDm\na/Vcxng1f44NzcfVi+sxB4BHAC6LJ1fH7D6XrjonLWeuziFnBoSz4Qhgc9VBJr3eiI2X5VOdH9kq\nyQInazznOArCzKB0rzPV2TmnmwPkUTvEdRnguvHhCxcuHK2prT5jGWuP1XYuzW/rAQ+ZLwMyD9HE\nhF+8pHGS4QgXoOobDmhY92DyPP4c7dwDcAVgTWf+lQGoArIDXiUwlbi6OwBWQB3puKrftY4OsKt8\neiTvUeXMgLBjwgy+cwAv7q1YcAU8fK7Hxkfycb+N9JxZxxF24sDn67MXSdwsdqUjnwsdsiEIB8IZ\n+1VQDuba+5j76GSjsv0A3gBiB1T7+/tHE4C8VxB2tlEw7tnTtXvoGQCsbdoDYrdxp6tlVXsAG52X\n66wVkEdJSI8Jj7LhCvxdvFegmbFhvW8OGJ9bJuwqEo2dgWdcx9dn+agRR3pmZSQjAKTigFqDxdXH\n5TPCChzIZOcqPVVGJuRCquEHHRsO8BnZqm8P86vS/BrzCHPf398/0ismBCMdk34jwxFVe1Wgp77O\nwOs6YicVGLvysj37vFsnzHqOxJBKdm31lJARmIrVurplYKv6Ofv38MWVPSJnBoRdIKtDusYLGQXe\nCswzR+AAqwLb5ZvpHMDD56s6VKDvgsR1TJUjjUhWV1fHDIDdKgkOCNVPj+Ov7flV6ZgAC8Bordkl\naPr6Oh+7z2MGAGdMuMeA2VauXhrkrr16wMv36P3uxYkKiCOtK5Ky1Uk6NKa+kPmGu0fTFfhqvDhA\nrfzfSQXKWT4njSOVMwXCpzkcMSLOyTk4ekAfert077eTSo/ZOUBj3bOAHBE3rpw5ZLBIXUbm/pwy\nmwtwwh8+jzoEQ9aP4AAPX5Sovt0QY8Ru4zzZfg48uY3Yd7OO/1EC2PlTBjyjT1qRb+WrHBdVJzEC\nuC7fETJVlZHZ3eWrAJ/pdRpAW8mZAmEHYifZ9N6qDGUB2mDMKHrAk/XUmmYGVDEVvr4nWVlapjpr\nJu637DOa7hsWjkXy43zUnb9ONiK6qoNf8NCvyrXWjgFvtucOgYddwp4KaNx2vT23idZVGWRvyKdi\nnSxz/MkdZ3pk5+O+3nGm4wibdTZzIOnW72dYUP3udGa9TkueOhDW+1zePSDW37JVBlkv3Wuk0Ucb\nZRyV3s6JVSobVNeG8GO8+4wm14GZr2OovFyuekRUYWBjYHDf9m2tpR/MqdLVi0MBwtVkpwNhFu2s\ntYPsvRSjtlFGp2nHxCvAGQXeKs6q+Kt0zHyh6rx6fu/Ww2fHWidXPsflachTBcJ6j+ZbOYMeay/s\ngi17VBk5p+x3BIz1/lFnqADX/ZZdr19we+aZZ+znNAEcrSrQv2Nn/ZlFu7Z3xxoM2jGoHtVX2Bh4\ndYyYfUWZcOUT2dKwnp0V9HTtc+bjvc40dFGg13xcvieNPa6bi7nQxfm/a2Nnq8wnuIys4xqpfxxr\n+80lPiPyVICwu17zrBzWOUDGVEfeQOJ8snRW3lzJHuVGgj67Njt2n9B0y92madr4+hiDMDtzMOEs\nSFxagS0AywEwsMneRyboqiEJnkitQFfTDIBxfwUICsDVm4lOsk49i5XT2Fz7ZYCm8ZDFVRbj+pvm\nV02iu/pnx2ozR3xOA4jPDAgDPvh7wOuu43MuDfTBtvptlAnP3btzVcBlAOzsWdliNK2M0711F6DB\nf+WuDDN0jwk19xZgtndByl+VU70VbKu0Az22tYLwCAA79qn24t9GAHgUjDNxgD9S1kicVXu2Zezd\nEwWvUVbduMzsOxyx52V0DNijgKx5cqxl6ZPImQFh51CV081xjMwZAA+02ZDDHAbsrtM0X39Scfcz\n0PVspvdU6QyEHRDHdxrmTMyNBHbGhN0HcwBssFsG24r5aoBzHfWNPfUV5zusj7PvXFDUts7AOGOW\nWq57BdxdV8Wb1s3ttRNTO7qXS0KnzJeZ9XJ+DoArG7j2iPw0nQGxtsuonCsQdufmArA6QOyzHvmk\nbLbHqk9imx7zze5jW5wkkCJ94cKF9FvGHBQ6UeaGI3hijmezOS/VO+7TcqM81YP1UfB1mys7hAGV\n9agAWN86405Iy+kx3x4ga/yoP2YA5uzUi60R33F+dHDw8N9c2KYKwnwN56Wgyu2m4KvXxqYd0ggg\nsw0rID6pnBsQ1uO5YKz7DByzsc6KvWZ5VVtW7yqwRnpX1zM7++nj3cg+xl61/hoUOtbqVkcwE+4F\nPB87AFYdoqMInUe2nm0rf3Ht65bSKZBnbdID3gwAM10z0XZzk5KjbcN5ahmxdxOwSnpislbzqHxO\n2yXO6zlXl6w+ihXZ/jTk3IDwKPDyfXou9s54+jjkxjznSgbA+ljmGlztkOmRMR4X7LFn9qO/6Z7z\niEd+B4QV23TLvlpr9j/u9Fg3Ll/rEIyTr6nYnjI/B17V08wIIFdt6Dqvk/h7JZkuCvY8PNPrCF1a\n6+WOXTsq+WEQVh2VxTom3APeCnzduQqATwuQzwwIA77xRgLT/c75uUAG8vFgXcOadRDZcRa42YRN\nBcRV55Gx9AqI2YF79uqVr/nxMIQLdHXusMdc8FG2zx+4572WXe1VJ+4s3W89JpwBn+qu9pvr25pW\n39C06pM9FcwB35H40HtZN2bB/HQUewZgp38G6iPg27Oxi8unlglXkhlTf4+021fiAtgFEufngLLK\n1+WteY7Ut3IELaNX99FAi3QEA4NeXMOAxkzG5af6uoBh1pzp7mzn6uc6Hdcx8OOy2rVaJz4Cwj39\neoDRs0OWdnXOQLeamHM2d347Vz/Az6dU9R3ZwlfZt9yT30hduUPX33i8n6XnvyznAoRZeo7tANXt\nWbIGzGZl9V5OR4O4vbt+ZCKG69YL8uycYwpcd3U0B+KO2fHYm477uvq6dG8s1NliVLL7OED1XAa6\n1VuSWjfuCCuWGeVW4FM93TCz5rKiPUJ4nL43Pq710bJc2U633v1xL8cJd/CuU1B7RRtWebNtT9K5\nsH76pJWx4qcWhCsAHvmtCpw41l7TOVZ27Nhx1bMqC9E0TxhVbKt6M4udhK8Px1JQUDDORDsrtWF1\nX5ZXZQtukxGp2s09PQD+X4Mz5sv3aiemZeuEVwZwPYLRu76yky7Jc2Bc6ZYdMwC5ulc6O+LDnYfG\ngfNtbosqbz7P+yrNwvpxPGWdwFMJwnMc1N2jjaUMYm6gV42lIMfXaw/M7MUBcOwzgOAyXZ3ZQSoA\nrgDC1T2zFwfUqGSdTzZ262ROB+065Qp0qwk3rkOW7o25VnUYYcFRTmb7OO/WSTvdRu2X6VfprPoq\nAHPeVeeVERKXd1WXUX/PynMADGDjxaGenHkQnsMQsuByaRZt5Lmsix1OQc6BsZbDIOOO457IJyYO\ndZjDsWFlKUD9331ZvdXZ9Ho+1gktFtceVUek6SwvFfYFDiL3+5yN69ZjU7EfAd+5LFjr5NqQ/aY3\nBKE2djppfKm/MQA78M2Yu3YeajfuHDjWHBNVe6gt5mKJxlXGvp9aJjzHYFWDzwFiYJPNzhVlmZq3\nSzu2lDEnBgHHajMZtaUCtLOBAxkOPg7kkX0GuK5j4iDs1dOxq7kTaxXbijq79tRj7lzYdo5NZf6c\n+TjrwqDDRKDyMb6mB5zOphXwuntZKl8b7bycTpF35JPZOaurIx1VHk8MhFtr3wrgywF8JoB7AH4Z\nwLdM0/TbdM27AHwx3TYB+HfTNL15IP856th7KvB1BsvYzNxH6shXZ2N7gOyCQh8VK4CtANjZJmPA\nWSBwZ+QcLWMFVVqPM9DNgNm1izun3yJQf8hWPKiuri7Acd+Zs/V0z8QBgeqioONsXHV8TqfRDol1\nzPRTe7HPKRnKOogo343Jaru42Hf7rO5ZB1O12+McjngtgO8G8OuH934HgHe21j5rmqZ7oTOA7wPw\nTwCElrszy+mK6/lGrqvu0bGpCuAzyQDYgTA7lwNgfdU37ledOO8Rp+LfNRCYPWo3Q8oAABU6SURB\nVFX1doxO88+CVs+pDarNSdZOwQYBDL0uy79xPr0O3LFdBx5ZuqpHxSydPuoDsXd69XR1ZajttC4V\nGDtw01hwQ3ejemneLj1CFJz02kHlsTHhaZpez8etta8D8IcAXgPg3fTT7jRNfzQn7znSA8MMQHtM\nmEV715GAYNboHuszVlyxYX2DifVXIHX1ccyJQUp1c6zXBbraytnOsUzdqvFKN3mkUgVilO0YKANw\nvJQzwpaqOlcgp/fNYVXchpW/Zrpl+unGTxrqM6GHtqvqWMWIxmWUqR1y5m9Vh+/qrf6iHVRGElzd\nM5tm8iTHhF+ONfP9mJz/2tba3wHwAoCfAfDtxJRPLKPst7p+pKdzDp/dx+cd+3UAzE7lHgn1U4uq\nh3MkZUHxe9WZBEPSSUTecx3ZLj2Gx2/DZUu+5gIwr3tV21dp15YVE+7lmeWrbV6x96weI8LlOlB3\nv2WssvdbxRwdE2b9KpByMabl9TqWrA1UJ02PzAuorqO2DnkiqyPaWtO3AXj3NE2/RT/9CIDfBfBh\nAJ8D4DsBfDqAr3yEsk7EfvU312jAWC/X632VXTKrzF6hzIJWwdjpomCYOb2ziXMyB8AZAxzZ4n4e\ns+PVHRpsGQBr2tWhB5bupYyKCfds5/zHtWPWdpWuJ5EeqFbA5H7jPFW0A3MdcwW+Tm9NjxCejFiM\n+qdj9dyxqF4j4M7ypJjw8wA+G8AX8slpmr6fDn+ztfYCgJ9vrb16mqYPZJllQXASltC7r+qpMyBz\nYKvHWmZvLa4DrmwcVK9zzDrTg+uRnXN7BWOus9ZBH8HVoSMPHfMbDaBsbFzbhtPasWRt64Ivsxff\nw5K1pfqJy9d1dvwb20n3VYfAurDNKhDJ/EeJDAPXKOhm4urA5bId9Jzek/mTDg1xPcI/gM0195zv\nnA4s8hmVE4Fwa+17ALwewGunafqDzuW/BqAB+DQAKQgn5eDixYsb/0Xm/uGXwUKd/SRADoyPFTsw\nVufPxDmGAheDV/XvxqO6Z7qqcLlcbwaWas9Mk1chuGNXLrddvMqqQDxHuE0iP2bWOhzBdq1AMiuL\n01me6p8KIHy/24feyvAUhEdeGuK9ttXIyyoso08Qle0q1tsT115sJ9eRjOapdZmm6ejPC1w9RmQ2\nCB8C8BsBfMk0TR8auOXzsB43LsH6ypUrx74jysDLj449B3HO0jO0M9ooEGfn3EoDvUd7/R4IOyCr\n6tarlwasy8tNKPb2JwVhrXtsyiwdK3b11U6RgV3rWPlQxcicVEFY+WsGlhmIOdDVevOxS+s59isX\nY1W9Ii8HWj0gdv43ApBZnllnUOHEaH6cvnbtGq5du7Zx7f7+Pm7fvt3VHZi/Tvh5AG8C8AYAO621\nVx7+dGuapr3W2qcC+BoAbwfwxwA+F8BbAfziNE3vKxW5eBEXL26qo2CbAVEveHpMZjSg3T2uARV0\nq3L18Sjqzb11ZgsXHD0WXOngdNYg57xOC4SrNozrGIQrYGF92e4KxHpeOzWXrtpy1OaubhkIu/pU\naWeT0XPadpUtejZg0HW/Zbbp5aniwL7SRwmP3jvSwXC6KvtxMuFvwJrVvkvOfz2AHwZwH8CXAfhH\nAG4A+D0APwHgn3cVGQBhDWSd2WYHAub3oj1Q6d3PIMpgHCDC9zhWwIEYwKDsxIFF5qAnPdYgin0A\nGO8re42CsAsOBd84VtbnNmcLBmBti7A1jw9y58d1Yh17Nnf38f2uLR2TVRDh6xyoaXtwfiPpHrFx\n9a2AV3V2x9W9lcxlwVl7ZPk6QpcdszzOdcL+2fHh778P4HVz8gx55plnUhBWh82AqHIc3mfBUu1H\nhcE36yldw/V69Io96T1ZELp0JpmDat4ZKJ8EhDkvDpgAx7AtA5Q7dlJdrx262keHYyrpXaO+qb7s\ngFfBVwG4KtcBcQXAjsiMAo/rHPTcadiwJxpLbM/MrzMgdvXOYpHlSa4TPjW5ePEiLl26tHHOVbR6\nRMrAN+thHeDOBWMXCMyoMkDOOgjnJL1AqAC4t3fgn+kwkl+keyDM5+L6AGAOGrWlAnLcx4DKKzRU\nRy6Dy42hLtfOmf9k4li5+qEGsoKwA+IKnLKxddXJ6ZflX/lDJiPgPBeUuQ6j5TtmPdqO6jsad44Y\nat699mA5MyDsmHAFsGqQ7DFK81MZeUSrRANNQVf3ASwVsFaditZjTp30XBxntnL6VUMQbIMKdPUY\nwDFwZDBlJspL97is0Mutkgn99O0sPo4y3OvNoyzYiQM2B8AOhN29PcDUdHafKyer55xOKLtWQa1i\n7tp5aj3m6uF8IStb0w6AM39mOZefssyYcOwrFliBbvYbS++RbVQq5hZ71pt7UMeKskedng5cjyzt\nmDBQrxJQoNXyeD8XhB0AKfjGOf7DUdXF6es6nZBI98amK5uNtonL15GJalPJOnvX+cd+BJCd7tU5\nZ5O5dtIyHBgrEOtvI+Js4vLWe7LO04GwrvSq5MyAcMaEee/OcRAp05kjc5iB3se6VW+cuc5DH3dc\n42ZOU52by3iyDiybNMrKnwPCWrdK7wDCsKv7nzse2nDtU7UldwZaf13Az79Xktk4C+geCLtVN5Vv\ncXoEfLO21N96YJWl+d4qDwXg6tpMnD9z/q7M7DgjSjGEFSu2WM4lCDsm7CRzfA4Yx+LmSsU+4nd3\njnVwYOw6Df4t7tM10hUzcuCodcgCUIdzVBd2OmW/FSM+CQizXlk6e9lCwXpOxxX3hUS9YnOTdT2Z\ny6iUcTvw1ScD5+fO711HNxeUKyAeZeMOTEfAOAPiXkeQ7V1dOD+XbwXADoTP7ZjwnN4DqI2VMbvR\nXlUBeKT31HP6OiQDc+iU6ciN7JiMm32tgqwXbK6jckHthlm43nFOHbYCZb5/hIX16pu1c88Woc9q\ntdoYk66C00nPb5xtwyaufdln+P5q6Ei3qu1HfKaqo+ql+4oEKQg6BjyHRVdPA5UOc+qXATLLuWTC\nlWROrYCXBaOyDAdqujH76ZXPjCADrio4OPDi/P7+vtU3yhsJFAfyfG3WYSkQ8HGv7MhLx7OrQOgB\nhOrAH/XJdNA2HxGnn9ONj7N8srbXvDNb62+ZTUZlLuhWbRtp9aWqXL3e6c4gm3WifA3nk/mapkcl\n88nqrUuW6jeVMw/C6nDOScI47tsCGhB8b+V8ztmzsmM/ykr4MZzrmQWjMhm31zTfU4GxAkSc1+Vi\nTi+1hTq+bk4XB7JZ+/BStAyIHQBXa4fZJplk7T0CwlnevY5nxB6Zrr3OfhSEq7oxEGZ6zDkeydPV\nk+9n/TjdA+GqQ+BrMjB2+Z7LdcJOnKNmx+qsIQy+Wb5VGXOui/LmbJyHe5NLX7MdsRdLBsDOgZ19\n1IGrYNI85wSBtqG+nJHtK/uH6FjrnABxemb15zqOsDFt4wpwR0A4A7E5eVbgmx0rWGX26YHsiJ9z\nHs72FQBnfp7lp9ep/2Q2AJ4iEAZqB+LfXVAy4OhSKL430rp3wFvpEPlnAOTOcz2Bh8MqEZQViFVA\nyud6oOv0GAH0Sidm/Blb0HIVZLN01gasQ8WEszHWrHPstbmru+7VBponTwCOAmUGGlyGY8I9sHd1\nyOrobNGLpcwXq07aAV6VzwgIa54OjDMAdvezPFXDESGZw2SMKEQZUeQ1dz/CUqK8EfDVYI99FsC9\ntKvvyLmszu5cBjKZbtXGZbg2zbY5QKirCqoAyjpFp2dmJ1d3LUvLy3w5s436SgbEWRkjYMz1UJu6\n81ln5+zGwiQps1Ulru2dj2V+pySlsqWzXVW/p54Ju8fWjB04h9HrqnRs7sUBPY5yKqetWLDTn+vR\n29w62ar3j2MHxL0OZpSFVEHFzl+BsI7/ZrbioAbqDylpADrbaDkZQDqpbOTqz8NODnB7nU8GKqGH\nA9zsXPhSpru2rbMp68VtwLbOfDWzVfYb65TpmbWH2ioDZK2HLtnU658aJuzAwY0NVr1OBgTOyO4a\n57QMHDyep+VVaS1Xx4NVpwgM3muw9Jy6Kt8FvNpay69ecKjq78SVGQDM/7fHkgVdpCMfB76VTVQv\ntQufV3FBP2J3tcNo2umbgUpFIPiYlx8qmHMbc310IjcjKWqrzH6ZZPXW+7ONr+sBr5apsRr3uHXk\nLwkm7EC4F2BV47pyuXx2TmUv6oCu3F4g9thPOD6z3QA7HmZRhqE66Hl1qMhHhwAYiKNMHsfslZO1\nQdW+CsD6H3Mj5ei6ZneddnbOFxxoKhhyXqMdkOaXlZed64GHAqICr0uH3eK8ghcDsQNoZyNmkCxZ\n3nOlB76cL+usNnQdCd8XwvM1aqP4fVTOPAiHVMGasVA+d9IG1rLC+RSAmSWPdAZZkDmmDzz8rGd8\n6St7Q8c5RI8VuFUZat/VanXsK2PZq7xZp9OzfTYOHAAcH0XJ1l1zOTwUMfIWo/MNBdlRNlp1wpwX\npxXARkA4EwUO9s9REA699ZjJwDPPPLPxSK71dLpHe7B9nK1C56qOI1L5pIJo1qFpp+c6WG3fp2o4\nogJeHi8M5wB871rN1FfgwIxXg7hylh4QR95czwyEAGwAsIKh67W57m7TSUrVS+3rnMo5Lefdsy2X\nV4F/AHDoEJ0Qt6tjZFG2A2C9Z45+bDdNK/jxuQqI+TjbZ+cynVmXDHAzEM4ASYE48tdHcufbAI49\nOamd1EddbJ2EaChwqr+4eGIds/Kztn1qmPDHP/5xvOxlL0udiLcwQvaIxI5TsTMXKK6sDNA1n6ys\n3d1dXL16daOcCvyiXtWnFh0Y8HWuM+IAYT1UF/efbHwt53nr1i284hWvsHZxomXGXociGIQZiDUo\ns2Eb13YMOllbKgjfvn0bzz333Cw2rPlpgGeAXKXdb66T5zpU4HtwcID79+/jypUrx9qXbeQ68owJ\nu85L/Ual6sQrcHQ66nb37l1cv37dAq8DZLVllO/IB8scJjz+lYktyJ/8yZ9sW4XHJvfu3du2Co9N\nnuZ2A4A7d+5sW4XHJnO+g3se5e7du9tW4ZicaRBeZJFFFnnaZQHhRRZZZJEtygLCiyyyyCJblLMw\nMXcVAPb29o79sFqtcO/ePaxWK0zTdLR3Kwjc5Ju+1MD7kJHZe7dels+FXiwuX520ePDgwbG6cR11\nQoMnIPlcnI+PS/OkidZZPzGZrcfVNC9Fii2WrV24cAH7+/tH6dVqhd3d3a5dQ7i8/f39Y5NxqgPX\nP+rA9Y7vAWcTfq4tdSUBTw7ykqyYsd/b20snm/hcJiMrMnrpOfmyrVTfsCv7cxzz8sSwBf/hQFzv\nJlJ1kjnydDHK52LymdvIHetqjmqFROR9cHCAF1988ZgvZJOWc2zMQhNzV6vrAKD1GvVxS2vtawD8\nyFaVWGSRRRZ5PPK10zT9aHXBWQDhTwDwNwB8EMBxOrzIIosscv7kKoC/AOAd0zT9cXXh1kF4kUUW\nWeSlLMvE3CKLLLLIFmUB4UUWWWSRLcoCwossssgiW5QFhBdZZJFFtihnEoRba9/YWvtAa+1ea+1X\nW2t/dds6nYa01t7SWjuQ7be2rddJpLX22tbaf26t/b/DerzBXPPPWmsfbq3tttb+S2vt07ah60mk\nV7/W2g+atnz7tvQdldbat7bW3tNau91a+0hr7adba58u11xprX1va+2jrbU7rbWfbK190rZ0niOD\n9XuXtNuqtfb8tnQ+cyDcWvtqAP8awFsAfB6A/wngHa21T9yqYqcn7wPwSgCvOty+aLvqnFhuAPgf\nAN4M4NgSm9batwD4BwD+PoDPB7CDdTtefpJKPoKU9TuUn8VmW77pyaj2SPJaAN8N4K8B+DIAlwC8\ns7V2ja55G4C/BeArAHwxgD8L4KeesJ4nlZH6TQC+Dw/b7s8A+OYnrCdpI2/PbHsD8KsA/g0dNwC/\nD+Cbt63bKdTtLQD++7b1eAz1OgDwBjn3YQDfRMc3AdwD8FXb1veU6veDAP7jtnU7hbp94mH9voja\n6UUAX07XfMbhNZ+/bX0ftX6H5/4rgLduW7fYzhQTbq1dAvAaAL8Q56a11X4ewBdsS69Tlr90+Ij7\nO621/9Ba+/PbVui0pbX2aqwZBrfjbQC/hqenHQHgdYePvP+7tfZ8a+1PbVuhE8jLsWaGHzs8fg3W\nnzPgtns/gA/hfLad1i/ka1trf9Ra+43W2r8QpvxE5Sx8O4LlEwE8A+Ajcv4jWPfG511+FcDXAXg/\n1o9A3wbgv7XW/vI0TTtb1Ou05VVYO75rx1c9eXUei/ws1o/oHwDwFwF8B4C3t9a+4JA4nHlp648g\nvA3Au6dpirmJVwG4f9hpspy7tkvqB6w/k/C7WD+tfQ6A7wTw6QC+8okribMHwk+1TNP0Djp8X2vt\nPVg7w1dh/Xi7yDmRaZp+nA5/s7X2GwB+B8DrsH7cPQ/yPIDPxvmdl+hJ1O8L+eQ0Td9Ph7/ZWnsB\nwM+31l49TdMHnqSCwNmbmPsogBXWA+YsrwTwwpNX5/HKNE23APw2gHOzamBQXsB6LP8l0Y4AcBi8\nH8U5acvW2vcAeD2A103T9GH66QUAl1trN+WWc9V2Ur8/6Fz+a1j761ba7kyB8DRNDwC8F8CXxrnD\nR4ovBfDL29LrcUlr7VmsH2V7TnKu5BCQXsBmO97Eesb6qWtHAGitfTKAT8A5aMtDgHojgL8+TdOH\n5Of3AtjHZtt9BoBPAfArT0zJR5BO/Zx8HtbDZ1tpu7M4HPFWAD/UWnsvgPcA+CYA1wH80DaVOg1p\nrf0rAD+D9RDEnwPwT7F2+B/bpl4nkdbaDayZQ3xc9VNba58L4GPTNP0e1mNx/7i19n+w/kLet2O9\nyuU/bUHd2VLV73B7C9Zjwi8cXvcvsX6qecfx3M6OHK6HfROANwDYaa3F08qtaZr2pmm63Vr7AQBv\nba19HMAdAN8F4JemaXrPdrQel179WmufCuBrALwdwB8D+FysMecXp2l63zZ03vryjGRZyZuxDtx7\nWPe+f2XbOp1SvX4MayC6h/Vs848CePW29TphXb4E66U/K9n+PV3zbVhPfuxiDU6ftm29T6N+WH+m\n8OewBuA9AP8XwL8F8Ke3rfdAvVydVgD+Ll1zBeu1th/FGoR/AsAnbVv306gfgE8G8C4Af3Tol+/H\nelL12W3pvHzKcpFFFllki3KmxoQXWWSRRV5qsoDwIossssgWZQHhRRZZZJEtygLCiyyyyCJblAWE\nF1lkkUW2KAsIL7LIIotsURYQXmSRRRbZoiwgvMgiiyyyRVlAeJFFFllki7KA8CKLLLLIFmUB4UUW\nWWSRLcoCwossssgiW5T/DwX8b8RcsnIGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ff41279b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BeF9r7dcvy/J8+6TeX2WQdNbw+fnmFx7U+uQ4PraepxralEFaj60QVgBXTVNd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ff40509e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# check image with noise and denoised image\\n",
    "# Better image if you train more or upgrade the model\\n",
    "\n",
    "img = image[0].cpu()\n",
    "input_img = image_n[0].cpu()\n",
    "output_img = output[0].cpu()\n",
    "\n",
    "origin = img.data.numpy()\n",
    "inp = input_img.data.numpy()\n",
    "out = output_img.data.numpy()\n",
    "\n",
    "plt.imshow(origin[0],cmap='gray')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(inp[0],cmap='gray')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(out[0],cmap=\"gray\")\n",
    "plt.show()\n",
    "\n",
    "print(label[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
